title
Power BI Full Course | Power BI Tutorial For Beginners | Power BI Course | Simplilearn

description
🔥Post Graduate Program In Data Analytics: https://www.simplilearn.com/pgp-data-analytics-certification-training-course?utm_campaign=PowerBIFullCourse-TBVss5711QM&utm_medium=Descriptionff&utm_source=youtube 🔥IIT Kanpur Professional Certificate Course In Data Analytics (India Only): https://www.simplilearn.com/iitk-professional-certificate-course-data-analytics?utm_campaign=PowerBIFullCourse-TBVss5711QM&utm_medium=Descriptionff&utm_source=youtube 🔥Caltech Data Analytics Bootcamp(US Only): https://www.simplilearn.com/data-analytics-bootcamp?utm_campaign=PowerBIFullCourse-TBVss5711QM&utm_medium=Descriptionff&utm_source=youtube 🔥Data Analyst Masters Program (Discount Code - YTBE15): https://www.simplilearn.com/data-analyst-masters-certification-training-course?utm_campaign=PowerBIFullCourse-TBVss5711QM&utm_medium=Descriptionff&utm_source=youtube In this video on the Power BI full course, we'll learn the basics of power bi and understand the different components of Power B, how to use Dax functions to derive value out of your data, and how to publish dashboards on to the power bi service. 🔥Free PowerBI Course: https://www.simplilearn.com/learn-power-bi-basics-free-course-skillup?utm_campaign=PowerBIFullCourse&utm_medium=Description&utm_source=youtube Dataset Link - https://drive.google.com/drive/folders/1De_OsIU_M_5-ttY-FQyzYsgnaI9-yDtq Below are the topics we will be discussing in this video: 00:00:00 What is Power BI 00:03:19 Features of Power BI 00:17:52 Power BI vs Tableau 00:31:55 Connecting data sources 00:50:13 DAX functions 02:36:57 Data Modeling 04:53:03 Power BI Dashboard ✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH ⏩ Check out the PowerBi training videos: https://bit.ly/35GuKQ5 #PowerBiFullCourse #PowerBiTutorialForBeginners #PowerBiCourse #LearnPowerBi StepByStep #PowerBi #TableauTrainingVideos #PowerBiTutorial #Simplilearn 🔥Explore our FREE Courses: https://www.simplilearn.com/skillup-free-online-courses?utm_campaign=PowerBiFullCourse&utm_medium=Description&utm_source=youtube ➡️ About Post Graduate Program In Data Analytics This Data Analytics Program is ideal for all working professionals and prior programming knowledge is not required. It covers topics like data analysis, data visualization, regression techniques, and supervised learning in-depth via our applied learning model with live sessions by leading practitioners and industry projects. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 8X higher live interaction in live online classes by industry experts - Capstone from 3 domains and 14+ Data Analytics Projects with Industry datasets from Google PlayStore, Lyft, World Bank etc. - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Resume preparation and LinkedIn profile building - 1:1 mock interview - Career accelerator webinars ✅ Skills Covered - Data Analytics - Statistical Analysis using Excel - Data Analysis Python and R - Data Visualization Tableau and Power BI - Linear and logistic regression modules - Clustering using kmeans - Supervised Learning 👉 Learn More at: https://www.simplilearn.com/pgp-data-analytics-certification-training-course?utm_campaign=PowerBIFullCourse-TBVss5711QM&utm_medium=Description&utm_source=youtube 🔥Caltech Data Analytics Bootcamp(US Only): https://www.simplilearn.com/data-analytics-bootcamp?utm_campaign=PowerBIFullCourse-TBVss5711QM&utm_medium=Description&utm_source=youtube 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688

detail
{'title': 'Power BI Full Course | Power BI Tutorial For Beginners | Power BI Course | Simplilearn', 'heatmap': [{'end': 943.649, 'start': 701.501, 'weight': 0.999}, {'end': 3519.65, 'start': 3276.375, 'weight': 0.713}], 'summary': 'The power bi full course covers an overview of power bi components and service, a comparison with tableau, data transformation, analysis, data modeling, and visualization techniques, including a superstore data analysis and power bi dashboard creation, aiming to enhance data processing efficiency and analysis.', 'chapters': [{'end': 1005.514, 'segs': [{'end': 114.478, 'src': 'embed', 'start': 89.199, 'weight': 6, 'content': [{'end': 96.124, 'text': 'In this video, you will learn why Power BI is needed, what is Power BI, the various features of Power BI, and the different components of Power BI.', 'start': 89.199, 'duration': 6.925}, {'end': 102.328, 'text': "Later, you will look at the architecture of Power BI, what Power BI's service is, and how to create a Power BI dashboard.", 'start': 96.504, 'duration': 5.824}, {'end': 106.792, 'text': 'Finally, you will understand a case study on Meyer and do a demo using Power BI.', 'start': 102.689, 'duration': 4.103}, {'end': 109.694, 'text': "Now let's understand why Power BI is needed.", 'start': 107.392, 'duration': 2.302}, {'end': 114.478, 'text': 'First, Power BI has the ability to access vast volumes of data from multiple sources.', 'start': 110.114, 'duration': 4.364}], 'summary': 'Power bi is essential for accessing and analyzing large volumes of data from various sources.', 'duration': 25.279, 'max_score': 89.199, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM89199.jpg'}, {'end': 217.606, 'src': 'embed', 'start': 187.486, 'weight': 3, 'content': [{'end': 191.427, 'text': 'It converts data from different sources to build interactive dashboards and BI reports.', 'start': 187.486, 'duration': 3.941}, {'end': 194.489, 'text': 'As you can see, we have an Excel data about sales.', 'start': 191.928, 'duration': 2.561}, {'end': 199.592, 'text': 'Using this data, Power BI helps you build different charts and graphs to visualize the data.', 'start': 195.229, 'duration': 4.363}, {'end': 204.356, 'text': 'Now that we have understood what Power BI is, let us look at the important features of Power BI.', 'start': 199.953, 'duration': 4.403}, {'end': 206.237, 'text': 'First is Power BI Desktop.', 'start': 204.856, 'duration': 1.381}, {'end': 212.382, 'text': 'Power BI Desktop is a free software that you can download and it allows you to build reports by accessing data easily.', 'start': 206.417, 'duration': 5.965}, {'end': 217.606, 'text': 'For using Power BI Desktop, you do not need advanced report designing or query skills to build a report.', 'start': 212.822, 'duration': 4.784}], 'summary': 'Power bi converts data from different sources to interactive dashboards, with power bi desktop enabling easy report building.', 'duration': 30.12, 'max_score': 187.486, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM187486.jpg'}, {'end': 943.649, 'src': 'heatmap', 'start': 675.672, 'weight': 1, 'content': [{'end': 677.413, 'text': "Let's start building our report now.", 'start': 675.672, 'duration': 1.741}, {'end': 678.773, 'text': "I'll go to my report view.", 'start': 677.753, 'duration': 1.02}, {'end': 680.834, 'text': 'So first let me create a text box.', 'start': 679.034, 'duration': 1.8}, {'end': 682.775, 'text': 'Let me resize it.', 'start': 682.015, 'duration': 0.76}, {'end': 687.689, 'text': 'Let me name it as finance dashboard.', 'start': 684.266, 'duration': 3.423}, {'end': 689.43, 'text': "We'll increase the size of the text.", 'start': 688.049, 'duration': 1.381}, {'end': 692.653, 'text': "We'll use font Consolus, center it.", 'start': 689.811, 'duration': 2.842}, {'end': 695.015, 'text': "We'll also add a background to this.", 'start': 692.893, 'duration': 2.122}, {'end': 700.9, 'text': 'We use blue color, change it to white and increase the size.', 'start': 695.415, 'duration': 5.485}, {'end': 704.723, 'text': 'Now, let me first show you how you can create a matrix.', 'start': 701.501, 'duration': 3.222}, {'end': 707.686, 'text': "I'll go to visualizations and click on matrix.", 'start': 705.404, 'duration': 2.282}, {'end': 708.947, 'text': 'Let me resize it.', 'start': 708.146, 'duration': 0.801}, {'end': 715.368, 'text': "From the Datasheet tab, I'll select Sales and drag on to Values.", 'start': 711.465, 'duration': 3.903}, {'end': 721.253, 'text': 'So, you can see the total number of sales that were made.', 'start': 718.691, 'duration': 2.562}, {'end': 723.455, 'text': 'Now, let me do some formatting.', 'start': 721.633, 'duration': 1.822}, {'end': 733.503, 'text': "So, I'll go to the Format tab, click on Column headers, let's add a background color and let me increase the text size to 20.", 'start': 723.795, 'duration': 9.708}, {'end': 738.447, 'text': "Similarly, under Values, we'll increase the size of the text to 20 as well.", 'start': 733.503, 'duration': 4.944}, {'end': 743.491, 'text': 'We can also click on border and choose the color of the border.', 'start': 739.708, 'duration': 3.783}, {'end': 746.212, 'text': 'We will take as, let it be black.', 'start': 743.511, 'duration': 2.701}, {'end': 751.216, 'text': 'So this is a simple matrix that we created which shows the total number of cells that were made.', 'start': 746.853, 'duration': 4.363}, {'end': 754.538, 'text': 'Similarly, let me choose matrix once again.', 'start': 751.576, 'duration': 2.962}, {'end': 758.584, 'text': "Now, we'll drag on the unit sold onto values.", 'start': 755.143, 'duration': 3.441}, {'end': 760.524, 'text': "We'll continue with the same drill.", 'start': 759.264, 'duration': 1.26}, {'end': 763.365, 'text': "Under column headers, we'll add a background.", 'start': 761.024, 'duration': 2.341}, {'end': 771.246, 'text': "This time, let's choose some other color and under values, let's increase the size of the text to 20.", 'start': 763.845, 'duration': 7.401}, {'end': 776.707, 'text': "Even for the column headers, let's increase the size of the text to 20.", 'start': 771.246, 'duration': 5.461}, {'end': 779.108, 'text': "Again, we'll switch on border.", 'start': 776.707, 'duration': 2.401}, {'end': 780.568, 'text': "We'll resize a bit.", 'start': 779.688, 'duration': 0.88}, {'end': 786.853, 'text': 'So here we have two matrix created for our report.', 'start': 783.53, 'duration': 3.323}, {'end': 789.796, 'text': 'The first matrix shows us the total sales that were made.', 'start': 787.574, 'duration': 2.222}, {'end': 793.599, 'text': 'The second matrix shows you the total units that were sold.', 'start': 790.176, 'duration': 3.423}, {'end': 797.342, 'text': "Now let's move ahead and create a simple bar chart.", 'start': 794.299, 'duration': 3.043}, {'end': 801.566, 'text': 'So under visualization, I click on clustered column chart.', 'start': 797.823, 'duration': 3.743}, {'end': 810.219, 'text': "Under this, we'll drag the date column onto axis and the sales onto value.", 'start': 804.474, 'duration': 5.745}, {'end': 811.741, 'text': 'Let me expand it.', 'start': 810.239, 'duration': 1.502}, {'end': 814.423, 'text': 'So, it shows you the sales by year.', 'start': 812.221, 'duration': 2.202}, {'end': 821.269, 'text': 'This is the sales that were made in 2013 and this shows you the sales that were made in 2014.', 'start': 814.903, 'duration': 6.366}, {'end': 824.511, 'text': "Now there's a drill down option which gives you more granularity.", 'start': 821.269, 'duration': 3.242}, {'end': 826.812, 'text': 'This depicts the sales by quarter.', 'start': 825.331, 'duration': 1.481}, {'end': 830.834, 'text': 'If I drill down further, you can see this shows you the sales by month.', 'start': 827.472, 'duration': 3.362}, {'end': 835.196, 'text': 'Also, you have some options like sort by and sort by sales.', 'start': 831.354, 'duration': 3.842}, {'end': 838.877, 'text': 'So you can see October month made the highest number of sales.', 'start': 835.796, 'duration': 3.081}, {'end': 844.1, 'text': 'Moving ahead, let me now create a pie chart where we will see the sales by different segments.', 'start': 839.758, 'duration': 4.342}, {'end': 846.841, 'text': "Under visualization, I'll click on pie chart.", 'start': 844.88, 'duration': 1.961}, {'end': 848.302, 'text': 'Let me first resize it.', 'start': 847.341, 'duration': 0.961}, {'end': 857.583, 'text': "Here, I'll drag the segment column onto the legend and the sales column onto the values.", 'start': 851.038, 'duration': 6.545}, {'end': 860.445, 'text': 'As you can see, we have the sales made by different segments.', 'start': 858.023, 'duration': 2.422}, {'end': 863.887, 'text': 'Government segment made the highest number of sales with 44.22%.', 'start': 861.125, 'duration': 2.762}, {'end': 868.29, 'text': 'Now, let me add a border to both the visualization.', 'start': 863.887, 'duration': 4.403}, {'end': 871.573, 'text': "I'll click on the pie chart and go to the format tab.", 'start': 868.771, 'duration': 2.802}, {'end': 873.874, 'text': "I'll switch on the border.", 'start': 872.393, 'duration': 1.481}, {'end': 880.196, 'text': "Similarly, for the clustered column chart, I'll go to the Format tab and click on Border.", 'start': 874.414, 'duration': 5.782}, {'end': 881.616, 'text': 'Let me resize a bit.', 'start': 880.616, 'duration': 1}, {'end': 888.078, 'text': "Alright Next, we'll create a very simple table that will depict the total sales made by each product.", 'start': 882.236, 'duration': 5.842}, {'end': 891.019, 'text': 'So, under Visualizations, I click on Table.', 'start': 888.619, 'duration': 2.4}, {'end': 892.36, 'text': 'Let me bring this below.', 'start': 891.48, 'duration': 0.88}, {'end': 899.746, 'text': "So from the data sheet, I'll first drag product onto values.", 'start': 895.663, 'duration': 4.083}, {'end': 904.13, 'text': 'You can see the different products and then sales just below it.', 'start': 900.427, 'duration': 3.703}, {'end': 908.513, 'text': 'So this depicts the total sales that were made by each product.', 'start': 905.591, 'duration': 2.922}, {'end': 912.196, 'text': 'And finally, it displays the total value of the sales that were made.', 'start': 908.794, 'duration': 3.402}, {'end': 914.758, 'text': 'This is same as the one shown here.', 'start': 912.777, 'duration': 1.981}, {'end': 916.66, 'text': "Now let's do some formatting.", 'start': 915.479, 'duration': 1.181}, {'end': 921.912, 'text': "Under format tab, I'll go to values and increase the text size to 15.", 'start': 917.168, 'duration': 4.744}, {'end': 923.513, 'text': 'Let me expand it.', 'start': 921.912, 'duration': 1.601}, {'end': 929.638, 'text': "Also under column headers, I'll increase the text size to 15.", 'start': 924.194, 'duration': 5.444}, {'end': 931.88, 'text': 'Then let me go and add a border.', 'start': 929.638, 'duration': 2.242}, {'end': 938.665, 'text': 'Now let me create a map that will show you the sales that were made by each country.', 'start': 934.201, 'duration': 4.464}, {'end': 943.649, 'text': "So first, let me create a new page and under visualization, I'll click on map.", 'start': 938.965, 'duration': 4.684}], 'summary': 'The report includes a finance dashboard, 2 matrices, a bar chart, a pie chart, a table, and a map.', 'duration': 57.831, 'max_score': 675.672, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM675672.jpg'}, {'end': 946.171, 'src': 'embed', 'start': 917.168, 'weight': 0, 'content': [{'end': 921.912, 'text': "Under format tab, I'll go to values and increase the text size to 15.", 'start': 917.168, 'duration': 4.744}, {'end': 923.513, 'text': 'Let me expand it.', 'start': 921.912, 'duration': 1.601}, {'end': 929.638, 'text': "Also under column headers, I'll increase the text size to 15.", 'start': 924.194, 'duration': 5.444}, {'end': 931.88, 'text': 'Then let me go and add a border.', 'start': 929.638, 'duration': 2.242}, {'end': 938.665, 'text': 'Now let me create a map that will show you the sales that were made by each country.', 'start': 934.201, 'duration': 4.464}, {'end': 943.649, 'text': "So first, let me create a new page and under visualization, I'll click on map.", 'start': 938.965, 'duration': 4.684}, {'end': 946.171, 'text': "Now I'll drag the country column.", 'start': 944.089, 'duration': 2.082}], 'summary': 'Format text to size 15, add border, create map showing sales by country.', 'duration': 29.003, 'max_score': 917.168, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM917168.jpg'}], 'start': 10.922, 'title': 'Power bi overview', 'summary': 'Provides an overview of power bi, including its components such as power query, power pivot, power view, power map, power bi desktop, and power q&a, as well as the functionality of power bi service in creating dashboards and reports, and its architecture involving data sources, gateway, and mobile apps.', 'chapters': [{'end': 263.011, 'start': 10.922, 'title': 'Power bi: leading bi tool', 'summary': "Explores power bi as a leading business intelligence tool, citing gartner's 2021 magic quadrant report, and delves into its crucial features, including its ability to access vast volumes of data from multiple sources, real-time stream analytics, and support for multiple data sources.", 'duration': 252.089, 'highlights': ["Power BI is a leader in the Gartner 2021 Magic Quadrant report for analytics and business intelligence platforms. This establishes Power BI's leading position in the industry, providing credibility and recognition for its capabilities.", 'Power BI allows access to vast volumes of data from multiple sources such as Excel, CSV, XML, JSON, and PDF. This feature enables users to handle large quantities of data that cannot be opened in Excel, enhancing data analysis and visualization capabilities.', 'Power BI supports real-time stream analytics, fetching data from multiple sensors and social media sources for access to real-time analytics. This capability ensures that users are equipped to make timely business decisions by leveraging real-time data insights.', 'Power BI provides support for multiple data sources, including Excel, CSV, SQL Server, and web files, to create interactive visualizations. The ability to access various data sources enhances the flexibility and richness of visualizations, contributing to more comprehensive data analysis.', 'Power BI offers custom visualization, providing access to a custom library of visualizations to meet specific needs when dealing with complex data. This feature ensures that users can tailor visualizations to their specific requirements, enhancing the interpretability and relevance of the data.']}, {'end': 534.261, 'start': 263.451, 'title': 'Power bi overview', 'summary': 'Provides an overview of power bi, including its components such as power query, power pivot, power view, power map, power bi desktop, and power q&a, as well as the functionality of power bi service in creating dashboards and reports, and its architecture involving data sources, gateway, and mobile apps.', 'duration': 270.81, 'highlights': ['Power BI Service enables users to create reports and dashboards, and ask questions to data, accessible via app.powerbi.com, with the ability to visualize data with various charts, graphs, and interactive visuals, and the functionality of creating single-page dashboards and tiles. Power BI Service provides the functionality to create reports and dashboards, ask questions to data, and visualize data with various charts and graphs, accessible via app.powerbi.com, and the ability to create single-page dashboards and tiles.', 'Power BI Desktop is a development tool for Power Query, Power Pivot, and Power View, providing a comprehensive solution for developing business intelligence and data analysis experiences. Power BI Desktop is a comprehensive development tool for Power Query, Power Pivot, and Power View, facilitating the development of business intelligence and data analysis experiences.', 'Power BI Architecture comprises multiple data sources, Power BI Desktop for creating reports and visualizations, Power BI Gateway for on-premise data sources, and cloud services for publishing reports and data visualizations, along with mobile apps available for Windows, iOS, and Android platforms. Power BI Architecture includes multiple data sources, Power BI Desktop for creating reports and visualizations, Power BI Gateway for on-premise data sources, cloud services for publishing reports and data visualizations, and mobile apps for Windows, iOS, and Android platforms.']}, {'end': 1005.514, 'start': 534.861, 'title': "Meijer's power bi success", 'summary': 'Demonstrates how meijer utilized power bi to analyze 20 billion rows of data in near real-time, leading to improved sales analysis, ad hoc reporting, and data visualization through practical hands-on demos.', 'duration': 470.653, 'highlights': ['Meijer connected Power BI to an on-premises SQL Server Analysis Services, enabling the refresh of 20 billion rows of data in near real-time. This demonstrates how Power BI improved data refresh speed and efficiency, allowing Meijer to perform real-time analysis with large datasets.', 'A bakery department at Meijer used Power BI to compare its sales with regional performance and sent out a sales flash to 800 business leaders. This shows how Power BI facilitated real-time sales analysis and reporting, leading to actionable insights and improved communication within the organization.', 'The chapter provides a practical hands-on demo of creating visualizations such as matrices, bar charts, pie charts, tables, maps, donut charts, and tree maps using Power BI. This highlights the practical application of Power BI in creating various visualizations to analyze and present data effectively.']}], 'duration': 994.592, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM10922.jpg', 'highlights': ['Power BI is a leader in the Gartner 2021 Magic Quadrant report for analytics and business intelligence platforms', 'Power BI allows access to vast volumes of data from multiple sources such as Excel, CSV, XML, JSON, and PDF', 'Power BI supports real-time stream analytics, fetching data from multiple sensors and social media sources', 'Power BI provides support for multiple data sources, including Excel, CSV, SQL Server, and web files', 'Power BI offers custom visualization, providing access to a custom library of visualizations', 'Power BI Service enables users to create reports and dashboards, and ask questions to data', 'Power BI Desktop is a development tool for Power Query, Power Pivot, and Power View', 'Power BI Architecture comprises multiple data sources, Power BI Desktop, Power BI Gateway, and cloud services', 'Meijer connected Power BI to an on-premises SQL Server Analysis Services, enabling the refresh of 20 billion rows of data in near real-time', 'A bakery department at Meijer used Power BI to compare its sales with regional performance and sent out a sales flash to 800 business leaders', 'The chapter provides a practical hands-on demo of creating visualizations using Power BI']}, {'end': 1808.187, 'segs': [{'end': 1099.597, 'src': 'embed', 'start': 1072.847, 'weight': 0, 'content': [{'end': 1079.031, 'text': 'In this video, we will learn about the two important data visualization and business intelligence tools, namely Power BI and Tableau.', 'start': 1072.847, 'duration': 6.184}, {'end': 1083.15, 'text': 'This video will help you learn the major differences between the two tools.', 'start': 1080.029, 'duration': 3.121}, {'end': 1088.572, 'text': 'First, we will look at the history of Power BI and Tableau followed by their cost.', 'start': 1083.85, 'duration': 4.722}, {'end': 1093.914, 'text': 'Then, we will see which of them has better performance and a good user interface.', 'start': 1089.333, 'duration': 4.581}, {'end': 1099.597, 'text': 'After that, we will look at the different data sources to which Power BI and Tableau can connect with.', 'start': 1095.235, 'duration': 4.362}], 'summary': 'Comparison between power bi and tableau, covering history, cost, performance, and data sources.', 'duration': 26.75, 'max_score': 1072.847, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM1072847.jpg'}, {'end': 1479.42, 'src': 'embed', 'start': 1447.958, 'weight': 3, 'content': [{'end': 1449.618, 'text': "Now let's talk about their ease of use.", 'start': 1447.958, 'duration': 1.66}, {'end': 1461.107, 'text': 'Power BI enjoys a slight edge in terms of ease of use because it is based on a user interface that has its roots in Microsoft Office 365.', 'start': 1451.659, 'duration': 9.448}, {'end': 1463.389, 'text': 'which most end users are already familiar with.', 'start': 1461.107, 'duration': 2.282}, {'end': 1469.633, 'text': 'Tableau provides some essential advantages for exploring and visualizing data in detail.', 'start': 1464.69, 'duration': 4.943}, {'end': 1474.036, 'text': 'Tableau is also incorporating natural language capabilities into its software.', 'start': 1470.294, 'duration': 3.742}, {'end': 1479.42, 'text': 'This will help us in finding solutions to complex problems by understanding the data better.', 'start': 1475.277, 'duration': 4.143}], 'summary': 'Power bi is slightly easier to use due to microsoft office 365 interface, while tableau offers advantages for exploring and visualizing data, and is adding natural language capabilities.', 'duration': 31.462, 'max_score': 1447.958, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM1447958.jpg'}, {'end': 1613.419, 'src': 'embed', 'start': 1585.141, 'weight': 1, 'content': [{'end': 1590.603, 'text': 'SQL Server based analysis services, data streaming in real time and many Azure database offers.', 'start': 1585.141, 'duration': 5.462}, {'end': 1596.306, 'text': 'It helps to understand the data and analyze the trends and patterns in the data.', 'start': 1592.624, 'duration': 3.682}, {'end': 1599.587, 'text': 'You can also forecast the data to make future predictions.', 'start': 1597.026, 'duration': 2.561}, {'end': 1604.309, 'text': 'Tableau supports the features of Python Machine Learning.', 'start': 1601.728, 'duration': 2.581}, {'end': 1608.17, 'text': 'This enables it to perform machine learning operations over the data set.', 'start': 1604.989, 'duration': 3.181}, {'end': 1613.419, 'text': "Finally, let's talk about customer support.", 'start': 1611.198, 'duration': 2.221}], 'summary': 'Sql server, azure databases, real-time data streaming, tableau with python ml, and customer support discussed.', 'duration': 28.278, 'max_score': 1585.141, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM1585141.jpg'}], 'start': 1006.114, 'title': 'Power bi vs tableau', 'summary': "Explores the differences between power bi and tableau, covering features, cost, performance, and market popularity, emphasizing power bi's cost-effectiveness and suitability for limited data volumes, while tableau excels in handling large data volumes and providing extensive visualization features.", 'chapters': [{'end': 1320.743, 'start': 1006.114, 'title': 'Power bi vs tableau: key differences', 'summary': 'Explains the key differences between power bi and tableau in terms of features, cost, performance, and history, highlighting that power bi is more cost-effective and better for limited data volumes, while tableau handles large volumes of data and provides extensive visualization features.', 'duration': 314.629, 'highlights': ['Power BI is way less expensive than Tableau, where the Pro version of Tableau comes at more than $35 per month per user, while Power BI Professional version costs less than $10 per month per user. Power BI Professional version costs less than $10 per month per user, while Tableau Pro version costs more than $35 per month per user.', 'Tableau can handle large volumes of data easily and provides extensive features for visualizing the data, while Power BI is faster and performs better when the volume of data is limited. Tableau can handle large volumes of data easily and provides extensive features for visualizing the data, while Power BI is faster and performs better when the volume of data is limited.', 'Power BI was originally designed by Ron George in the summer of 2010 and the initial release was available for public download on 11th of July 2011, while Tableau software was founded in 2003 in Mountain View, California and the Tableau Desktop 1.0 was released in 2004. Power BI was originally designed by Ron George in the summer of 2010 and the initial release was available for public download on 11th of July 2011, while Tableau software was founded in 2003 in Mountain View, California and the Tableau Desktop 1.0 was released in 2004.']}, {'end': 1808.187, 'start': 1320.743, 'title': 'Comparing power bi and tableau for business intelligence', 'summary': 'Compares power bi and tableau in terms of user interface, data sources, ease of use, programming support, data visualization, machine learning, customer support, google trends, and gartner magic quadrant, with a focus on features, advantages, and market popularity.', 'duration': 487.444, 'highlights': ['Tableau is more popular these days with high search volume, as shown in the Google Trends over the last 5 years, from 2015 onwards. Tableau is a clear frontrunner with high search volume, indicating its current popularity.', 'Gartner has recognized Microsoft as a Magic Quadrant leader in Analytics and Business Intelligence platforms for 13 consecutive years, positioning it ahead of Tableau in this aspect. Microsoft has been consistently recognized as a leader in Analytics and Business Intelligence platforms by Gartner for 13 consecutive years.', 'Tableau has over 160,000 active users participating in over 500 global user groups and over 150,000 active customers participating in the Tableau online community, indicating a larger and more active user base compared to Power BI. Tableau has a larger and more active user base with over 160,000 active users and over 150,000 active customers participating in its online community.', 'Power BI supports various data sources but has limited access to other databases and servers compared to Tableau, while Tableau has access to numerous data sources and servers, offering a wider range of connectivity options. Tableau has broader access to numerous data sources and servers compared to Power BI, providing a wider range of connectivity options.', 'Power BI provides an easy to use drag and draw functionality, a wide range of detailed and attractive visualizations, and the ability to ask questions about data and receive meaningful insights using Power BI service. Power BI offers an easy-to-use interface with drag and draw functionality, a wide range of detailed visualizations, and the ability to ask questions and gain meaningful insights using Power BI service.']}], 'duration': 802.073, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM1006114.jpg', 'highlights': ['Tableau can handle large volumes of data easily and provides extensive features for visualizing the data.', 'Tableau has over 160,000 active users and over 150,000 active customers participating in its online community.', 'Tableau is more popular these days with high search volume, as shown in the Google Trends over the last 5 years.', 'Power BI is way less expensive than Tableau, with the Professional version costing less than $10 per month per user.', 'Power BI offers an easy-to-use interface with drag and draw functionality and a wide range of detailed visualizations.']}, {'end': 3988.818, 'segs': [{'end': 1886.912, 'src': 'embed', 'start': 1858.239, 'weight': 4, 'content': [{'end': 1866.679, 'text': "let's understand what are the different components or what are the different ways in which you can work on your Power BI.", 'start': 1858.239, 'duration': 8.44}, {'end': 1876.624, 'text': 'Now, one of the main challenges when it comes to organizations or users is that data is scattered in different places.', 'start': 1866.959, 'duration': 9.665}, {'end': 1883.407, 'text': 'It might be in different formats and anyone, everyone would want to use that data.', 'start': 1877.404, 'duration': 6.003}, {'end': 1886.912, 'text': 'basically perform some calculations,', 'start': 1884.148, 'duration': 2.764}], 'summary': 'Power bi offers various ways to work with scattered data in different formats, allowing users to perform calculations.', 'duration': 28.673, 'max_score': 1858.239, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM1858239.jpg'}, {'end': 2095.15, 'src': 'embed', 'start': 2065.313, 'weight': 0, 'content': [{'end': 2067.074, 'text': 'Now, how do you get this kind of email??', 'start': 2065.313, 'duration': 1.761}, {'end': 2069.797, 'text': 'Because when you talk about Power BI,', 'start': 2067.475, 'duration': 2.322}, {'end': 2080.857, 'text': 'it will expect you to have A official ID and it does not take IDs which are from common domains such as Google or Yahoo, and so on.', 'start': 2069.797, 'duration': 11.06}, {'end': 2086.163, 'text': 'So this particular link gives you an idea how you can do that.', 'start': 2081.297, 'duration': 4.866}, {'end': 2090.949, 'text': 'So basically you have what is power bi basic explanation on that.', 'start': 2086.864, 'duration': 4.085}, {'end': 2095.15, 'text': 'It says signing up for Power BI service or Power BI desktop.', 'start': 2091.467, 'duration': 3.683}], 'summary': 'To access power bi, use an official id, not from common domains like google or yahoo.', 'duration': 29.837, 'max_score': 2065.313, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM2065313.jpg'}, {'end': 2147.447, 'src': 'embed', 'start': 2114.179, 'weight': 2, 'content': [{'end': 2117.862, 'text': 'Or you can go for this one, which says enroll U.S. government organization.', 'start': 2114.179, 'duration': 3.683}, {'end': 2121.945, 'text': 'And this is where you can basically sign up for Power BI.', 'start': 2117.882, 'duration': 4.063}, {'end': 2124.868, 'text': 'So it basically says try free.', 'start': 2122.426, 'duration': 2.442}, {'end': 2128.391, 'text': 'If you go to the website, say powerbi.microsoft.com.', 'start': 2125.128, 'duration': 3.263}, {'end': 2132.954, 'text': 'Or you could go into this one, which I was saying, http://app.com.', 'start': 2128.991, 'duration': 3.963}, {'end': 2142.983, 'text': 'your powerbi.com, and this is what you can use.', 'start': 2138.939, 'duration': 4.044}, {'end': 2147.447, 'text': 'or, as mentioned, you can go to powerbi.microsoft.com.', 'start': 2142.983, 'duration': 4.464}], 'summary': 'The transcript discusses enrolling in power bi, with multiple references to powerbi.microsoft.com and a free trial offer.', 'duration': 33.268, 'max_score': 2114.179, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM2114179.jpg'}, {'end': 2645.678, 'src': 'embed', 'start': 2614.294, 'weight': 7, 'content': [{'end': 2623.15, 'text': 'so when we look at the visualization pane, that basically allows us to create different kinds of visualizations here.', 'start': 2614.294, 'duration': 8.856}, {'end': 2624.651, 'text': 'And we will understand that.', 'start': 2623.45, 'duration': 1.201}, {'end': 2638.076, 'text': 'So you can basically visualize on your different data and create different kinds of charts graphs, maps and basically derive insights from your data.', 'start': 2625.331, 'duration': 12.745}, {'end': 2645.678, 'text': "Now, when you talk about data models, that's where you can basically establish relationships.", 'start': 2638.476, 'duration': 7.202}], 'summary': 'Visualize and derive insights from different data using various charts and graphs.', 'duration': 31.384, 'max_score': 2614.294, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM2614294.jpg'}, {'end': 2795.075, 'src': 'embed', 'start': 2765.102, 'weight': 6, 'content': [{'end': 2771.95, 'text': "Let's say database or let's say some kind of files or let's say some kind of systems what you have.", 'start': 2765.102, 'duration': 6.848}, {'end': 2775.08, 'text': 'So your data might be coming in from different places.', 'start': 2772.557, 'duration': 2.523}, {'end': 2779.164, 'text': 'Now you might want to transform the data.', 'start': 2775.46, 'duration': 3.704}, {'end': 2783.908, 'text': 'So this is where I could say there is your query editor, which I was explaining.', 'start': 2779.244, 'duration': 4.664}, {'end': 2795.075, 'text': 'So you have your query editor, which basically allows you edit the data table or basically allows you to edit the data before it is loaded right.', 'start': 2784.549, 'duration': 10.526}], 'summary': 'Query editor allows data transformation before loading.', 'duration': 29.973, 'max_score': 2765.102, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM2765102.jpg'}, {'end': 2984.413, 'src': 'embed', 'start': 2959.725, 'weight': 5, 'content': [{'end': 2967.076, 'text': 'So what we do is we build a relationship between both the tables using a common key column, which exists in both cases.', 'start': 2959.725, 'duration': 7.351}, {'end': 2969.3, 'text': 'As I said, there is a product key here.', 'start': 2967.377, 'duration': 1.923}, {'end': 2971.203, 'text': 'There is a product key here.', 'start': 2969.901, 'duration': 1.302}, {'end': 2976.409, 'text': 'so that basically allows us to have a relationship between these two.', 'start': 2971.727, 'duration': 4.682}, {'end': 2978.95, 'text': 'now that could be one to one.', 'start': 2976.409, 'duration': 2.541}, {'end': 2982.932, 'text': 'this could also be related to other tables, so it could be one too many.', 'start': 2978.95, 'duration': 3.982}, {'end': 2984.413, 'text': 'you could have many to one.', 'start': 2982.932, 'duration': 1.481}], 'summary': 'Building relationships between tables using a common key column enables one-to-one or one-to-many relationships.', 'duration': 24.688, 'max_score': 2959.725, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM2959725.jpg'}, {'end': 3519.65, 'src': 'heatmap', 'start': 3276.375, 'weight': 0.713, 'content': [{'end': 3282.959, 'text': 'so, for example, i can go for excel And basically click on this.', 'start': 3276.375, 'duration': 6.584}, {'end': 3285.9, 'text': 'Now here are some of my data sets.', 'start': 3283.339, 'duration': 2.561}, {'end': 3289.622, 'text': "So let's look into the folder here.", 'start': 3286.48, 'duration': 3.142}, {'end': 3296.306, 'text': "And these data sets are also available on my GitHub link, which I'll share with you later.", 'start': 3289.642, 'duration': 6.664}, {'end': 3299.849, 'text': 'so here we have something called a superstore.', 'start': 3296.906, 'duration': 2.943}, {'end': 3301.311, 'text': "let's click on this.", 'start': 3299.849, 'duration': 1.462}, {'end': 3308.918, 'text': 'and here i already have a data set which is global superstore, or i also have selective data.', 'start': 3301.311, 'duration': 7.607}, {'end': 3311.401, 'text': "so let's select this one.", 'start': 3308.918, 'duration': 2.483}, {'end': 3312.262, 'text': 'click on open.', 'start': 3311.401, 'duration': 0.861}, {'end': 3320.218, 'text': "now that's basically connecting to my data source and that will show me what does that excel sheet have.", 'start': 3313.61, 'duration': 6.608}, {'end': 3325.424, 'text': 'so it has different tabs, which is orders, people and returns.', 'start': 3320.218, 'duration': 5.206}, {'end': 3332.838, 'text': "so let's select orders and that basically gives me a preview of the data which I have.", 'start': 3325.424, 'duration': 7.414}, {'end': 3343.953, 'text': 'and if you scroll all the way to right, it shows me city, state and then country, and if you see here, we do see information of all the countries.', 'start': 3332.838, 'duration': 11.115}, {'end': 3351.36, 'text': 'now this can be huge amount of data which may, which we would want look into, but say, for example,', 'start': 3344.654, 'duration': 6.706}, {'end': 3359.668, 'text': "my use case is that i'm interested in looking for the data for united states and as a country and all its states.", 'start': 3351.36, 'duration': 8.308}, {'end': 3362.79, 'text': 'so we will do that when we do a transform.', 'start': 3359.668, 'duration': 3.122}, {'end': 3370.126, 'text': 'now we can also select the returns tab and that shows me these three fields.', 'start': 3362.79, 'duration': 7.336}, {'end': 3378.368, 'text': 'however, the first row should have been the heading of this particular data set and we will transform that.', 'start': 3370.126, 'duration': 8.242}, {'end': 3382.609, 'text': 'now i can go ahead and click on load, but that will load all the data.', 'start': 3378.368, 'duration': 4.241}, {'end': 3385.269, 'text': "so instead of that, let's go for transforming.", 'start': 3382.609, 'duration': 2.66}, {'end': 3387.67, 'text': "so let's click on transform data now.", 'start': 3385.269, 'duration': 2.401}, {'end': 3391.01, 'text': 'once you do that, it brings you your toolkit.', 'start': 3387.67, 'duration': 3.34}, {'end': 3396.071, 'text': 'that brings you your power query editor, which allows you to transform your data.', 'start': 3391.01, 'duration': 5.061}, {'end': 3403.693, 'text': 'so, for example, we have our data from returns tab or returns data source, as you see here.', 'start': 3396.071, 'duration': 7.622}, {'end': 3411.014, 'text': 'so we see column 1, column 2 and column 3, and that also shows the type of the data here.', 'start': 3403.693, 'duration': 7.321}, {'end': 3414.315, 'text': 'it also gives me a quick small option here.', 'start': 3411.014, 'duration': 3.301}, {'end': 3418.916, 'text': "let's select this and then i can say use first row as header.", 'start': 3414.315, 'duration': 4.601}, {'end': 3421.887, 'text': 'Now there are various other options which you can do.', 'start': 3419.304, 'duration': 2.583}, {'end': 3424.11, 'text': 'You can add a custom column.', 'start': 3422.368, 'duration': 1.742}, {'end': 3426.113, 'text': 'You can add column with examples.', 'start': 3424.21, 'duration': 1.903}, {'end': 3427.715, 'text': 'You can keep the top rows.', 'start': 3426.193, 'duration': 1.522}, {'end': 3430.059, 'text': 'You can remove the top rows.', 'start': 3428.399, 'duration': 1.66}, {'end': 3432.12, 'text': 'You can keep errors, keep duplicates.', 'start': 3430.119, 'duration': 2.001}, {'end': 3433.28, 'text': 'So there are different ways.', 'start': 3432.32, 'duration': 0.96}, {'end': 3436.601, 'text': 'And you can also do a merge query or append query.', 'start': 3433.36, 'duration': 3.241}, {'end': 3440.502, 'text': "So as of now, let's just say use first row as headers.", 'start': 3436.821, 'duration': 3.681}, {'end': 3445.484, 'text': 'And that basically shows that now my first row has become the header.', 'start': 3441.322, 'duration': 4.162}, {'end': 3454.386, 'text': 'You also see in the applied steps, it basically tells me if I have changed the type of the data.', 'start': 3445.984, 'duration': 8.402}, {'end': 3459.667, 'text': 'if i have made any other changes, those steps will get added here.', 'start': 3455.106, 'duration': 4.561}, {'end': 3461.428, 'text': 'so it basically shows me the name.', 'start': 3459.667, 'duration': 1.761}, {'end': 3469.13, 'text': "as it returns, it shows me applied steps where i've changed the type and now i basically have this information.", 'start': 3461.428, 'duration': 7.702}, {'end': 3476.552, 'text': 'now this is something where you can change the type or you can basically set it to a particular format.', 'start': 3469.13, 'duration': 7.422}, {'end': 3482.914, 'text': 'however, we are not doing anything of that sort right now, so we can do that for any of these columns.', 'start': 3476.552, 'duration': 6.362}, {'end': 3483.654, 'text': 'so this looks good.', 'start': 3482.914, 'duration': 0.74}, {'end': 3493.82, 'text': "when it comes to orders, let's click on this and, as i said, i would be interested in selecting for country as united states only,", 'start': 3484.654, 'duration': 9.166}, {'end': 3496.421, 'text': 'and let me just work on that data.', 'start': 3493.82, 'duration': 2.601}, {'end': 3503.886, 'text': 'however, we can work on all the data, so let me scroll all the way to right, and here i have the country.', 'start': 3496.421, 'duration': 7.465}, {'end': 3506.83, 'text': 'Now there are these filters which we can use.', 'start': 3504.426, 'duration': 2.404}, {'end': 3512.198, 'text': 'So basically I can click on this and that shows me all the countries are selected.', 'start': 3507.531, 'duration': 4.667}, {'end': 3519.65, 'text': 'Now there is also something called as text filters which we will see how we can use to select particular data.', 'start': 3512.278, 'duration': 7.372}], 'summary': 'Using excel, the speaker demonstrates data transformation and filtering, focusing on united states data.', 'duration': 243.275, 'max_score': 3276.375, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM3276375.jpg'}], 'start': 1809.087, 'title': 'Power bi and tableau', 'summary': 'Covers learning tableau and power bi, including tableau statistics, building interactive dashboards, arithmetic, logical and lod calculations, heatmap, waterfall, pareto, clustering, forecasting, power bi server and service features, data modeling, visualization, dax functions, quick demo, data transformation, and data visualization and analysis.', 'chapters': [{'end': 2409.847, 'start': 1809.087, 'title': 'Learn tableau and power bi', 'summary': "Covers learning tableau and power bi, including tableau statistics, building interactive dashboards, arithmetic, logical and lod calculations, heatmap, waterfall, pareto, clustering, forecasting, and power bi's components and licensing, with a focus on setting up power bi on a machine and different ways to sign up for power bi.", 'duration': 600.76, 'highlights': ['The chapter covers learning Tableau and Power BI, including Tableau statistics, building interactive dashboards, arithmetic, logical and LOD calculations, heatmap, waterfall, Pareto, clustering, forecasting. The course covers a range of topics including Tableau statistics, building interactive dashboards, arithmetic, logical and LOD calculations, and various visualization techniques such as heatmap, waterfall, Pareto, clustering, and forecasting.', "It discusses Power BI's components such as Power BI desktop, server/service, and mobile, and their respective functions, emphasizing the importance of bringing all data in one place, transforming it, and making it selective to work on. The chapter explains Power BI's components such as Power BI desktop, server/service, and mobile, highlighting their roles in bringing all data in one place, transforming it, making it selective to work on, and enabling ETL, data modeling, storage, and reporting.", 'It provides guidance on setting up Power BI on a machine, signing up for Power BI service, and the types of email addresses supported by Power BI. The transcript offers guidance on setting up Power BI on a machine, signing up for Power BI service, and the types of email addresses supported by Power BI, emphasizing the process of signing up for Power BI service and the limitations of using private email IDs.', 'It explains the different ways to sign up for Power BI, including signing up for Power BI service as an individual, using the Power BI website, app, or enrolling in a U.S. government organization, and provides detailed steps for signing up for Power BI with a new Microsoft 365 trial account. The chapter details the different ways to sign up for Power BI, such as signing up for Power BI service as an individual, using the Power BI website, app, or enrolling in a U.S. government organization, and provides step-by-step guidance for signing up for Power BI with a new Microsoft 365 trial account.', "It gives insights into the licensing options for Power BI, including the pro version's limitations and the features of the premium account, with a focus on the pricing and limitations of the pro version, and the conditional-based features of the premium account. The chapter provides insights into the licensing options for Power BI, including the pricing and limitations of the pro version, and the conditional-based features of the premium account, highlighting the differences in features and limitations between the pro version and the premium account."]}, {'end': 2614.294, 'start': 2409.847, 'title': 'Power bi server and service features', 'summary': 'Discusses the features of power bi server and service, including sharing reports, anomaly detection, automation of reports, security implementation, connecting to different data sources, and visualization options.', 'duration': 204.447, 'highlights': ['Power BI Server and Service allows sharing reports and making them available to different BUs. It enables users to share reports online and make them available to different business units.', 'Power BI provides various features such as anomaly detection, automation of reports, and role-based security implementation. It offers features like anomaly detection, automation of reports, and role-based security implementation.', 'Power BI supports connecting to different data sources and provides various visualization options. It allows connecting to different data sources and offers a wide range of visualization options.', "The Power BI also includes 'insert' for adding different visuals and 'transform data' for ETL work. It includes features like 'insert' for adding different visuals and 'transform data' for ETL work.", 'Power BI allows creating new tables, working on data, and managing relationships through the modeling feature. It provides the capability to create new tables, work on data, and manage relationships through the modeling feature.']}, {'end': 3011.008, 'start': 2614.294, 'title': 'Data modeling and visualization in power bi', 'summary': 'Explains how to create visualizations and establish data models in power bi, emphasizing the importance of building relationships between different data sources and tables to derive insights and enable efficient data analysis.', 'duration': 396.714, 'highlights': ['Establishing relationships between data sources and tables is essential for efficient data analysis in Power BI. The chapter emphasizes the importance of building relationships between different data sources and tables to enable efficient data analysis and derive insights.', 'Power BI allows for creating various visualizations such as charts, graphs, and maps to derive insights from different data sources. The visualization pane in Power BI enables users to create a wide range of visualizations, including charts, graphs, and maps, to derive insights from different data sources.', 'The process of data modeling involves connecting multiple data sources to build relationships, facilitating the extraction of information from different tables and data sets. Data modeling in Power BI involves connecting multiple data sources to build relationships, facilitating the extraction of information from different tables and data sets for analysis.']}, {'end': 3214.283, 'start': 3011.208, 'title': 'Dax functions in power bi', 'summary': 'Introduces dax functions in power bi, explaining their use in creating calculated columns and measures, and their impact on file size, with examples of different types of dax functions and their applications.', 'duration': 203.075, 'highlights': ['DAX functions can be used to create calculated columns and measures in Power BI, impacting the file size and offering more power to work on data. DAX functions provide more power to work on data, impacting file size by appending values to each row in a table and storing them in the model.', 'The difference between calculated columns and measures in Power BI, with measures not increasing the file size and not creating new data in the tables themselves. Measures do not increase the file size or create new data in the tables, unlike calculated columns.', 'Explanation of different types of DAX functions including date and time, logical, text, statistical, and information functions. The chapter explains the different types of DAX functions, including date and time, logical, text, statistical, and information functions, each with specific applications.']}, {'end': 3476.552, 'start': 3214.964, 'title': 'Power bi quick demo', 'summary': 'Covers a quick demo of using power bi, including uploading and transforming datasets, selecting specific data, and using the power query editor to transform and manipulate the data.', 'duration': 261.588, 'highlights': ['The demo covers uploading and transforming datasets, including selecting specific data and using the Power Query Editor to manipulate the data. The demo showcases the process of uploading and transforming datasets, focusing on selecting specific data and utilizing the Power Query Editor to manipulate and transform the data, providing a practical demonstration of Power BI capabilities.', 'The process involves using the Power Query Editor to transform the data, including options such as adding custom columns, keeping top rows, removing errors and duplicates, and changing data types. The use of the Power Query Editor allows for various data transformation options, such as adding custom columns, keeping or removing top rows, errors, and duplicates, and changing data types, providing a comprehensive toolset for data manipulation.', 'The demo emphasizes the ability to change data types and formats within the Power Query Editor, with applied steps tracking the changes made to the data. The emphasis on changing data types and formats within the Power Query Editor is highlighted, with applied steps tracking the alterations made to the data, showcasing the flexibility and control available in data manipulation.', 'The process showcases the practical utility of Power BI in handling and visualizing large datasets, with a specific focus on selecting and transforming data based on user-defined criteria. The practical utility of Power BI in handling and visualizing large datasets is demonstrated, with a focus on selecting and transforming data based on specific user-defined criteria, showcasing the adaptability and efficiency of Power BI in real-world scenarios.', 'The demo includes accessing datasets from both the internet and GitHub, providing access to a wide range of data sources for analysis and visualization. Accessing datasets from both the internet and GitHub is a key aspect of the demo, highlighting the versatility and accessibility of data sources available for analysis and visualization within Power BI.']}, {'end': 3677.498, 'start': 3476.552, 'title': 'Power bi data transformation', 'summary': 'Demonstrates the process of filtering and transforming data in power bi, including selecting specific countries and applying filters, resulting in the successful upload of selective data from orders and returns data sources.', 'duration': 200.946, 'highlights': ['The applied steps indicate the presence of filtered rows after the basic transformation process for the orders and returns data sources.', 'The process includes filtering for United States as the country of interest, resulting in the display of data exclusively from the United States.', 'The successful application of changes in the query editor leads to the upload of selective data from the orders and returns data sources.']}, {'end': 3988.818, 'start': 3677.498, 'title': 'Data visualization and analysis', 'summary': 'Demonstrates the process of creating a report with visualizations, utilizing filters and slices to manipulate the data, and customizing the visualization format to enhance readability and analysis.', 'duration': 311.32, 'highlights': ['The process of creating a report with visualizations The speaker discusses the steps involved in creating a report with visualizations, demonstrating the utilization of various visualization options and fields to generate a comprehensive report.', 'Utilizing filters and slices to manipulate the data The demonstration includes the utilization of filters and slices to manipulate the data, such as selecting specific fields like country and state, and applying advanced filtering options to refine the data further.', 'Customizing the visualization format to enhance readability and analysis The speaker explains how to customize the visualization format by adjusting grid settings, font size, and color, as well as modifying column headers and field formatting to enhance the readability and analysis of the data.']}], 'duration': 2179.731, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM1809087.jpg', 'highlights': ['The chapter covers learning Tableau and Power BI, including Tableau statistics, building interactive dashboards, arithmetic, logical and LOD calculations, heatmap, waterfall, Pareto, clustering, forecasting. (Relevance: 10)', 'The course covers a range of topics including Tableau statistics, building interactive dashboards, arithmetic, logical and LOD calculations, and various visualization techniques such as heatmap, waterfall, Pareto, clustering, and forecasting. (Relevance: 9)', 'The chapter provides insights into the licensing options for Power BI, including the pricing and limitations of the pro version, and the conditional-based features of the premium account, highlighting the differences in features and limitations between the pro version and the premium account. (Relevance: 8)', 'The process of creating a report with visualizations, demonstrating the utilization of various visualization options and fields to generate a comprehensive report. (Relevance: 7)', 'The demo showcases the process of uploading and transforming datasets, focusing on selecting specific data and utilizing the Power Query Editor to manipulate and transform the data, providing a practical demonstration of Power BI capabilities. (Relevance: 6)', 'The process involves using the Power Query Editor to transform the data, including options such as adding custom columns, keeping or removing top rows, errors, and duplicates, and changing data types, providing a comprehensive toolset for data manipulation. (Relevance: 5)', 'The chapter emphasizes the importance of building relationships between different data sources and tables to enable efficient data analysis and derive insights. (Relevance: 4)', 'DAX functions provide more power to work on data, impacting file size by appending values to each row in a table and storing them in the model. (Relevance: 3)', 'Data modeling in Power BI involves connecting multiple data sources to build relationships, facilitating the extraction of information from different tables and data sets for analysis. (Relevance: 2)', 'The use of the Power Query Editor allows for various data transformation options, such as adding custom columns, keeping or removing top rows, errors, and duplicates, and changing data types, providing a comprehensive toolset for data manipulation. (Relevance: 1)']}, {'end': 5501.762, 'segs': [{'end': 4042.764, 'src': 'embed', 'start': 4013.053, 'weight': 0, 'content': [{'end': 4023.802, 'text': "i can also make it bigger so i could select a particular field if i'm more interested in looking at the information for a particular state.", 'start': 4013.053, 'duration': 10.749}, {'end': 4027.084, 'text': 'now i can add more fields to this.', 'start': 4023.802, 'duration': 3.282}, {'end': 4032.408, 'text': 'so this is my one of the reports which i have created.', 'start': 4027.084, 'duration': 5.324}, {'end': 4040.342, 'text': 'now what i can do with this report is i can basically have more data fields.', 'start': 4032.408, 'duration': 7.934}, {'end': 4042.764, 'text': 'i can add filters to this.', 'start': 4040.342, 'duration': 2.422}], 'summary': 'Report can be expanded with more fields and filters for detailed state-specific data analysis.', 'duration': 29.711, 'max_score': 4013.053, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM4013053.jpg'}, {'end': 4095.107, 'src': 'embed', 'start': 4068.246, 'weight': 2, 'content': [{'end': 4073.73, 'text': 'if you are interested in, you can do a sort by country, sales or state.', 'start': 4068.246, 'duration': 5.484}, {'end': 4084.52, 'text': "so, for example, let's do a sorting by state and that basically gives me the data which has been sorted by state information.", 'start': 4073.73, 'duration': 10.79}, {'end': 4091.985, 'text': 'now we could obviously have the information here, so I can then change the order.', 'start': 4084.52, 'duration': 7.465}, {'end': 4093.486, 'text': 'so it shows me alphabetically.', 'start': 4091.985, 'duration': 1.501}, {'end': 4095.107, 'text': 'this is the information which I have.', 'start': 4093.486, 'duration': 1.621}], 'summary': 'Data can be sorted by country, sales, or state, with the ability to change the order and display alphabetically.', 'duration': 26.861, 'max_score': 4068.246, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM4068246.jpg'}, {'end': 4192.139, 'src': 'embed', 'start': 4161.211, 'weight': 9, 'content': [{'end': 4169.457, 'text': 'that will open up in Power BI and we have created a simple report which we have basically used by taking our data.', 'start': 4161.211, 'duration': 8.246}, {'end': 4175.862, 'text': 'now I can also do is I can publish this if I would want to share this information.', 'start': 4169.457, 'duration': 6.405}, {'end': 4180.866, 'text': 'so publish this report online in Power BI service.', 'start': 4175.862, 'duration': 5.004}, {'end': 4189.595, 'text': 'you can basically select your report, what we have here, and i clicked on publish.', 'start': 4180.866, 'duration': 8.729}, {'end': 4192.139, 'text': "so it says what's the destination.", 'start': 4189.595, 'duration': 2.544}], 'summary': 'Created a report in power bi and published it online.', 'duration': 30.928, 'max_score': 4161.211, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM4161211.jpg'}, {'end': 4300.171, 'src': 'embed', 'start': 4269.235, 'weight': 4, 'content': [{'end': 4274.079, 'text': 'now i can click on this one straight away and that takes me to my service.', 'start': 4269.235, 'duration': 4.844}, {'end': 4281.805, 'text': 'now, once it takes me to the service, it shows me the report which we created, which we published,', 'start': 4274.079, 'duration': 7.726}, {'end': 4290.351, 'text': 'and it basically has the option where i can save it as a a different copy or give a different name.', 'start': 4281.805, 'duration': 8.546}, {'end': 4294.054, 'text': 'i can embed this in diff in a website or a portal.', 'start': 4290.351, 'duration': 3.703}, {'end': 4300.171, 'text': 'i can publish to web Embed this report for public access by anyone on the internet.', 'start': 4294.054, 'duration': 6.117}], 'summary': 'Access and publish reports, embed in website or portal, and enable public web access.', 'duration': 30.936, 'max_score': 4269.235, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM4269235.jpg'}, {'end': 5009.838, 'src': 'embed', 'start': 4980.829, 'weight': 3, 'content': [{'end': 4993.269, 'text': 'Now, here you have an option, as we saw earlier, that is get data, or what you can do is you have an option where you can create new data.', 'start': 4980.829, 'duration': 12.44}, {'end': 4995.35, 'text': 'also, it says enter data.', 'start': 4993.269, 'duration': 2.081}, {'end': 5001.833, 'text': 'now this is something which can be easily used if you have relatively less number of fields.', 'start': 4995.35, 'duration': 6.483}, {'end': 5009.838, 'text': 'so you can basically add more columns here and you can basically add values.', 'start': 5001.833, 'duration': 8.005}], 'summary': 'Option to get or create data, add new columns and values easily.', 'duration': 29.009, 'max_score': 4980.829, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM4980829.jpg'}], 'start': 3988.818, 'title': 'Power bi data visualization and reporting', 'summary': 'Covers creating visual reports, customizing reports, creating power bi reports, data visualization capabilities, and data manipulation using power bi, offering insights into data visualization and reporting, enabling collaboration and data exploration, and providing a lot of options for data manipulation and reporting.', 'chapters': [{'end': 4068.246, 'start': 3988.818, 'title': 'Data visualization and reporting', 'summary': 'Discusses creating and customizing visual reports, adding data fields and filters, and exporting data, providing insights into data visualization and reporting capabilities.', 'duration': 79.428, 'highlights': ['The chapter discusses creating and customizing visual reports, adding data fields and filters, and exporting data, providing insights into data visualization and reporting capabilities.', 'The visuals provide sales information and allow for customization, such as scrolling, enlarging, and selecting specific fields and states, offering a user-friendly experience.', 'The option to export data or view it as a table enhances the versatility and functionality of the reporting capabilities, enabling efficient data analysis and sharing.']}, {'end': 4393.723, 'start': 4068.246, 'title': 'Power bi report creation', 'summary': 'Demonstrates the process of creating a power bi report, including sorting data, saving the report, publishing it to power bi service, and sharing options, enabling collaboration and data exploration.', 'duration': 325.477, 'highlights': ['The process of creating a Power BI report, including sorting and visualizing data, is demonstrated. The speaker shows how to sort data by country or state, visualize the data, and save it as a Power BI file, highlighting the process of creating a Power BI report.', "The report is saved as a Power BI file named 'first report' and can be accessed on the machine. The report is saved as 'first report' and can be accessed on the machine, demonstrating the saving process.", 'The report is published to Power BI service, enabling collaboration and sharing with different sources. The report is published to Power BI service, allowing collaboration and sharing with different sources, showcasing the sharing process.', 'Options for sharing the report, including sharing to teams and setting up common pains, are demonstrated. The speaker demonstrates options for sharing the report, such as sharing to teams, setting up common pains, and viewing usage metrics report.']}, {'end': 4893.556, 'start': 4393.723, 'title': 'Power bi data report', 'summary': "Discusses the process of creating a data report using power bi, including adding data fields, applying filters, creating relationships between fields, and modifying the report's format.", 'duration': 499.833, 'highlights': ['Creating a report and applying filters to analyze data The speaker demonstrates adding data fields, applying filters based on sales amount, and modifying the report to show relevant information, providing insights into the process of analyzing data.', 'Publishing and saving the report The speaker explains the process of saving and modifying the report, showcasing the functionality of publishing and saving a copy of the report from the Power BI service.', 'Viewing usage metrics and report insights The chapter discusses accessing usage metrics, including views per day and unique viewers per day, and the ability to analyze data for quick insights, providing valuable information for report analysis and performance evaluation.', 'Creating relationships between fields and fixing display issues The speaker demonstrates the process of creating relationships between different data sets, fixing display issues by defining relationships, and utilizing filters to generate an insightful report, showcasing the troubleshooting and data analysis capabilities of Power BI.', 'Formatting and customizing the report The speaker showcases the process of formatting the report, including setting the grid option and color, demonstrating the customization capabilities of Power BI for creating visually appealing reports.']}, {'end': 5501.762, 'start': 4894.356, 'title': 'Power bi data transformation', 'summary': 'Discusses how to modify a report by rearranging the order of columns, creating new data sets with multiple tables, and performing data transformations and type conversions using power bi, offering a lot of options for data manipulation and reporting.', 'duration': 607.406, 'highlights': ['Power BI allows for modifying reports by rearranging column orders and adding new data sets with multiple tables, offering a lot of options for data manipulation and reporting. The speaker demonstrates how to modify a report by rearranging the order of columns and creating new data sets with multiple tables using Power BI.', 'The chapter illustrates how to perform data transformations and type conversions using Power BI, providing various options for manipulating data and performing computations. The transcript details the process of performing data transformations and type conversions using Power BI, such as changing data types and performing computations on columns.', 'Power BI enables users to easily add, edit, and remove data, as well as perform data cleanup and transformation operations, offering a user-friendly interface for data manipulation. The speaker explains how to add, edit, and remove data, as well as perform data cleanup and transformation operations using Power BI, demonstrating the user-friendly interface for data manipulation.']}], 'duration': 1512.944, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM3988818.jpg', 'highlights': ['The visuals provide sales information and allow for customization, offering a user-friendly experience.', 'Options for sharing the report, including sharing to teams and setting up common pains, are demonstrated.', 'Viewing usage metrics and report insights, including views per day and unique viewers per day, provides valuable information for report analysis and performance evaluation.', 'Creating relationships between fields and fixing display issues showcases the troubleshooting and data analysis capabilities of Power BI.', 'Formatting and customizing the report demonstrates the customization capabilities of Power BI for creating visually appealing reports.', 'The chapter discusses creating and customizing visual reports, adding data fields and filters, and exporting data, providing insights into data visualization and reporting capabilities.', 'The option to export data or view it as a table enhances the versatility and functionality of the reporting capabilities, enabling efficient data analysis and sharing.', 'The process of creating a Power BI report, including sorting and visualizing data, is demonstrated, highlighting the process of creating a Power BI report.', 'The report is published to Power BI service, enabling collaboration and sharing with different sources, showcasing the sharing process.', "The report is saved as a Power BI file named 'first report' and can be accessed on the machine, demonstrating the saving process.", 'Power BI allows for modifying reports by rearranging column orders and adding new data sets with multiple tables, offering a lot of options for data manipulation and reporting.', 'Power BI enables users to easily add, edit, and remove data, as well as perform data cleanup and transformation operations, offering a user-friendly interface for data manipulation.', 'The chapter illustrates how to perform data transformations and type conversions using Power BI, providing various options for manipulating data and performing computations.']}, {'end': 6269.019, 'segs': [{'end': 5529.368, 'src': 'embed', 'start': 5502.182, 'weight': 0, 'content': [{'end': 5507.946, 'text': 'But then if I have more fields like month and days and so on, then you can do that.', 'start': 5502.182, 'duration': 5.764}, {'end': 5512.169, 'text': 'So here it also has the option.', 'start': 5508.367, 'duration': 3.802}, {'end': 5516.993, 'text': 'So when you have selected this, you have an option called transform and transpose.', 'start': 5512.19, 'duration': 4.803}, {'end': 5521.677, 'text': 'Sorry, transform also has various options which allow you to work on these.', 'start': 5517.534, 'duration': 4.143}, {'end': 5524.399, 'text': 'Do you want to do some scientific calculations??', 'start': 5522.117, 'duration': 2.282}, {'end': 5529.368, 'text': 'Do you want to work on? the date field?', 'start': 5524.459, 'duration': 4.909}], 'summary': 'Demonstrates the use of transform and transpose options for data manipulation.', 'duration': 27.186, 'max_score': 5502.182, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM5502182.jpg'}, {'end': 5695.722, 'src': 'embed', 'start': 5668.97, 'weight': 1, 'content': [{'end': 5675.515, 'text': "We should basically transform the data so that you don't end up loading everything and you don't work on all the data.", 'start': 5668.97, 'duration': 6.545}, {'end': 5677.637, 'text': 'I mean, unless you really want to.', 'start': 5675.675, 'duration': 1.962}, {'end': 5682.76, 'text': "So here I will, this Excel sheet, which I'm talking about has two different tabs.", 'start': 5678.357, 'duration': 4.403}, {'end': 5683.961, 'text': 'We have used it before.', 'start': 5682.84, 'duration': 1.121}, {'end': 5685.703, 'text': "So let's use orders.", 'start': 5684.482, 'duration': 1.221}, {'end': 5687.844, 'text': "Let's use returns.", 'start': 5685.803, 'duration': 2.041}, {'end': 5692.12, 'text': 'And that basically shows me The data, what we have.', 'start': 5688.845, 'duration': 3.275}, {'end': 5695.722, 'text': "so I can do a load, but that's not what we would want to do.", 'start': 5692.12, 'duration': 3.602}], 'summary': 'Transform data to load specific tabs like orders and returns to avoid loading unnecessary data.', 'duration': 26.752, 'max_score': 5668.97, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM5668970.jpg'}, {'end': 6269.019, 'src': 'embed', 'start': 6243.209, 'weight': 5, 'content': [{'end': 6249.797, 'text': "I can say I'm interested in sales, which is the data here.", 'start': 6243.209, 'duration': 6.588}, {'end': 6256.325, 'text': 'We can basically look into the quantities of sales is something which we are interested in.', 'start': 6250.458, 'duration': 5.867}, {'end': 6262.794, 'text': 'But might be we are interested in sales, which are more than a particular value.', 'start': 6256.789, 'duration': 6.005}, {'end': 6265.236, 'text': "I'm not interested in lower amount of sales.", 'start': 6262.994, 'duration': 2.242}, {'end': 6269.019, 'text': 'I would want to look into United States and UK data,', 'start': 6265.756, 'duration': 3.263}], 'summary': 'Interest in sales data, focusing on quantities and specific values, targeting united states and uk.', 'duration': 25.81, 'max_score': 6243.209, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM6243209.jpg'}], 'start': 5502.182, 'title': 'Data transformation in power bi', 'summary': 'Covers using power bi for data transformation, including basic transformations and loading a large dataset, with emphasis on the importance of making basic transformations before loading the data. it also demonstrates data filtering and transformation processes, focusing on selecting specific columns, filtering data based on country, and renaming fields for further analysis in the context of united states and united kingdom data.', 'chapters': [{'end': 5718.253, 'start': 5502.182, 'title': 'Working with data in power bi', 'summary': 'Discusses using power bi to transform and work on data, including performing basic transformations and loading a large dataset from the global superstore, while emphasizing the importance of making basic transformations before loading the data.', 'duration': 216.071, 'highlights': ['Using Power BI to transform and work on data The transcript discusses the option of transforming and transposing data in Power BI, as well as performing scientific calculations and working on date fields.', 'Importance of making basic transformations before loading large datasets The chapter emphasizes the importance of making basic transformations before loading a large dataset from the global superstore in Power BI, to avoid unnecessary loading and processing of data.', 'Loading a large dataset from the global superstore The chapter mentions loading a large dataset from the global superstore in Power BI, which contains data from various countries, products, and sales.']}, {'end': 6269.019, 'start': 5718.253, 'title': 'Data filtering and transformation', 'summary': 'Demonstrates the process of filtering and transforming data, focusing on selecting specific columns, filtering data based on country, and renaming fields for further analysis in the context of united states and united kingdom data.', 'duration': 550.766, 'highlights': ['The chapter demonstrates filtering and transforming data based on specific criteria The chapter provides a detailed walkthrough of filtering and transforming data based on specific criteria, such as selecting specific columns, filtering based on country, and renaming fields.', 'The focus is on selecting specific columns, filtering data based on country, and renaming fields for further analysis The chapter emphasizes the process of selecting specific columns, filtering data based on country (United States and United Kingdom), and renaming fields for further analysis and insights.', 'The demonstration involves filtering data to focus on United States and United Kingdom data The demonstration involves filtering data to focus specifically on United States and United Kingdom data for further analysis and insights.']}], 'duration': 766.837, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM5502182.jpg', 'highlights': ['Importance of making basic transformations before loading large datasets', 'The focus is on selecting specific columns, filtering data based on country, and renaming fields for further analysis', 'The demonstration involves filtering data to focus on United States and United Kingdom data', 'Using Power BI to transform and work on data', 'The chapter demonstrates filtering and transforming data based on specific criteria', 'Loading a large dataset from the global superstore']}, {'end': 7568.737, 'segs': [{'end': 6318.644, 'src': 'embed', 'start': 6291.531, 'weight': 2, 'content': [{'end': 6297.157, 'text': 'so we have selected some data, we have create, done some transformations.', 'start': 6291.531, 'duration': 5.626}, {'end': 6302.758, 'text': "we can basically break a particular column into multiple columns if that's what we want.", 'start': 6297.157, 'duration': 5.601}, {'end': 6313.802, 'text': 'if we see that we will do an aggregation based on year or we will do a aggregation based on the country or year and order id right,', 'start': 6302.758, 'duration': 11.044}, {'end': 6317.443, 'text': 'so we can break this data into multiple columns.', 'start': 6313.802, 'duration': 3.641}, {'end': 6318.644, 'text': 'so we can do that.', 'start': 6317.443, 'duration': 1.201}], 'summary': 'Data selected, transformed, and can be broken into multiple columns for aggregation.', 'duration': 27.113, 'max_score': 6291.531, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM6291531.jpg'}, {'end': 6421.939, 'src': 'embed', 'start': 6392.415, 'weight': 6, 'content': [{'end': 6404.61, 'text': "so we have all the fields showing up from orders, and let's also look at the returns, which basically has the columns, the order id and your region.", 'start': 6392.415, 'duration': 12.195}, {'end': 6408.512, 'text': 'so it basically shows the region here.', 'start': 6404.61, 'duration': 3.902}, {'end': 6411.674, 'text': 'where was the product returned from?', 'start': 6408.512, 'duration': 3.162}, {'end': 6413.475, 'text': 'and we have this information.', 'start': 6411.674, 'duration': 1.801}, {'end': 6421.939, 'text': 'so what we are doing is we, when we were doing a text filter in the previous example, remember it was case sensitive.', 'start': 6413.475, 'duration': 8.464}], 'summary': 'Analyzing orders and returns data, focusing on region and product return information.', 'duration': 29.524, 'max_score': 6392.415, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM6392415.jpg'}, {'end': 6651.413, 'src': 'embed', 'start': 6621.516, 'weight': 0, 'content': [{'end': 6627.98, 'text': "now i can keep the filter because i'm interested only in these values or i can filter out.", 'start': 6621.516, 'duration': 6.464}, {'end': 6635.224, 'text': 'so this tells me that when we did a and it is basically going for laser and copier, right.', 'start': 6627.98, 'duration': 7.244}, {'end': 6640.167, 'text': "so let's say, for example, clear all filters, the thing is gone.", 'start': 6635.224, 'duration': 4.943}, {'end': 6651.413, 'text': "what we can do is let's go here, go for text filters, say contains, and here i will say it can be a copier, okay,", 'start': 6640.167, 'duration': 11.246}], 'summary': 'Filtering for laser and copier, using text filter for copier.', 'duration': 29.897, 'max_score': 6621.516, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM6621516.jpg'}, {'end': 6881.735, 'src': 'embed', 'start': 6855.584, 'weight': 5, 'content': [{'end': 6860.107, 'text': 'Okay, now I can basically duplicate the column.', 'start': 6855.584, 'duration': 4.523}, {'end': 6868.853, 'text': "So I really don't want to work on this column itself, but it would be good to have duplicate column on which we can work on.", 'start': 6860.127, 'duration': 8.726}, {'end': 6872.353, 'text': 'so i can do a split column here if i would want to.', 'start': 6868.853, 'duration': 3.5}, {'end': 6874.094, 'text': "but let's create a duplicate column.", 'start': 6872.353, 'duration': 1.741}, {'end': 6877.354, 'text': "so let's say duplicate column now.", 'start': 6874.094, 'duration': 3.26}, {'end': 6881.735, 'text': 'that gives me a duplicate column, so we can rename it later.', 'start': 6877.354, 'duration': 4.381}], 'summary': 'Duplicated column created for flexibility in data manipulation.', 'duration': 26.151, 'max_score': 6855.584, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM6855584.jpg'}, {'end': 6937.591, 'src': 'embed', 'start': 6909.827, 'weight': 1, 'content': [{'end': 6916.73, 'text': 'if you see, it basically identifies the delimiter, which is hyphen or dash.', 'start': 6909.827, 'duration': 6.903}, {'end': 6926.374, 'text': "now you can go for split at left most, Identify leftmost, delimiter, rightmost, each occurrence of delimiter, and that's what we want to do.", 'start': 6916.73, 'duration': 9.644}, {'end': 6930.482, 'text': 'You can look into advanced options where it says split into columns.', 'start': 6926.815, 'duration': 3.667}, {'end': 6937.591, 'text': "So do you want to split that into columns? Do you want to split that into rows? Because that's more or less like doing a group by.", 'start': 6931.465, 'duration': 6.126}], 'summary': 'The process involves identifying the delimiter, splitting into columns or rows, similar to a group by.', 'duration': 27.764, 'max_score': 6909.827, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM6909827.jpg'}, {'end': 7475.263, 'src': 'embed', 'start': 7442.122, 'weight': 4, 'content': [{'end': 7443.842, 'text': 'so i can just say save.', 'start': 7442.122, 'duration': 1.72}, {'end': 7447.464, 'text': 'i can go for save, as i have different other options.', 'start': 7443.842, 'duration': 3.622}, {'end': 7454.987, 'text': 'so if you would want to perform, keep performing your transformation, then you can just do this.', 'start': 7447.464, 'duration': 7.523}, {'end': 7458.795, 'text': 'you can add a column, you can view the data.', 'start': 7455.854, 'duration': 2.941}, {'end': 7467.979, 'text': 'so for now, our transformations are good enough and what we can do is we can basically go back to home,', 'start': 7458.795, 'duration': 9.184}, {'end': 7475.263, 'text': 'we can do a close and apply and we will be back to our data set, which has been modified.', 'start': 7467.979, 'duration': 7.284}], 'summary': 'Options for performing transformations and returning to modified data set discussed.', 'duration': 33.141, 'max_score': 7442.122, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM7442122.jpg'}], 'start': 6269.019, 'title': 'Data transformation and analysis', 'summary': 'Explores data transformation and analysis processes, including selecting, transforming, aggregating, and loading data, using power query editor for filtering and transforming data, covering various data transformation tasks and merging data columns, emphasizing preservation of original data and creating meaningful column names.', 'chapters': [{'end': 6392.415, 'start': 6269.019, 'title': 'Data transformation and analysis', 'summary': 'Explores the process of transforming and analyzing data, including selecting, transforming, aggregating, and loading data, and creating aggregations and filters, to enable efficient data analysis and decision-making.', 'duration': 123.396, 'highlights': ['The process involves selecting, transforming, and loading data, and creating aggregations and filters to enable efficient data analysis and decision-making.', 'The chapter emphasizes the importance of making changes once the data set is uploaded to avoid multiple transformations.', 'Aggregating data based on different criteria such as year, country, or order id allows for in-depth analysis and decision-making.', 'The ability to create custom aggregations and filters provides flexibility in data analysis and decision-making.']}, {'end': 6832.23, 'start': 6392.415, 'title': 'Data transformation and filtering', 'summary': 'Discusses data transformation and filtering techniques using power query editor, demonstrating filtering by product name and quantity, and transforming data by splitting the order id column, facilitating efficient data analysis and manipulation.', 'duration': 439.815, 'highlights': ['The speaker demonstrates filtering techniques by applying text filters to show rows containing specific product names and quantities, refining data analysis and visualization.', 'The chapter emphasizes the process of transforming data by splitting the order ID column, enabling efficient data manipulation and analysis.', 'The speaker illustrates the use of Power Query Editor to connect, prepare, and transform the data, showcasing the versatility and functionality of the tool.', 'The chapter highlights the importance of using precise filters and matching field names for effective data analysis and visualization.']}, {'end': 7127.168, 'start': 6832.23, 'title': 'Data transformation and analysis', 'summary': 'Covers various data transformation tasks including changing data types, using first row as header, replacing values, running merge queries, and performing analytics using split, duplicate, and renaming columns in a dataset.', 'duration': 294.938, 'highlights': ['The chapter covers various data transformation tasks including changing data types, using first row as header, replacing values, running merge queries, and performing analytics using split, duplicate, and renaming columns in a dataset.', 'The process involves duplicating a column, splitting it by delimiter, and potentially splitting it into columns or rows, allowing for better organization and analysis of the data.', 'The example demonstrates the process of creating a copy of a column, splitting it, and then renaming the resulting columns, providing a hands-on understanding of the data transformation tasks.']}, {'end': 7568.737, 'start': 7127.208, 'title': 'Data transformation and merging', 'summary': 'Covers splitting and merging data columns, with an emphasis on preserving original data and creating meaningful column names. it also discusses the process of applying and saving transformations in a data set.', 'duration': 441.529, 'highlights': ['The chapter covers splitting and merging data columns with an emphasis on preserving original data. The speaker explains the process of splitting data based on a delimiter and creating new columns. They highlight the importance of preserving the original data by creating duplicates and renaming them.', 'The chapter discusses creating meaningful column names and applying and saving transformations in a data set. The speaker emphasizes the significance of renaming columns to make more sense and following a naming convention. They also discuss the process of applying and saving the transformations in the data set.', 'The chapter emphasizes the process of applying and saving transformations in a data set. The speaker explains the process of applying the changes and saving the modified data set, highlighting the steps involved in performing and saving transformations.']}], 'duration': 1299.718, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM6269019.jpg', 'highlights': ['The process involves selecting, transforming, and loading data, and creating aggregations and filters to enable efficient data analysis and decision-making.', 'The chapter covers various data transformation tasks including changing data types, using first row as header, replacing values, running merge queries, and performing analytics using split, duplicate, and renaming columns in a dataset.', 'The chapter covers splitting and merging data columns with an emphasis on preserving original data. The speaker explains the process of splitting data based on a delimiter and creating new columns. They highlight the importance of preserving the original data by creating duplicates and renaming them.', 'The speaker illustrates the use of Power Query Editor to connect, prepare, and transform the data, showcasing the versatility and functionality of the tool.', 'The chapter emphasizes the importance of using precise filters and matching field names for effective data analysis and visualization.', 'The ability to create custom aggregations and filters provides flexibility in data analysis and decision-making.', 'The chapter emphasizes the process of transforming data by splitting the order ID column, enabling efficient data manipulation and analysis.', 'The example demonstrates the process of creating a copy of a column, splitting it, and then renaming the resulting columns, providing a hands-on understanding of the data transformation tasks.', 'Aggregating data based on different criteria such as year, country, or order id allows for in-depth analysis and decision-making.', 'The chapter emphasizes the importance of making changes once the data set is uploaded to avoid multiple transformations.']}, {'end': 8274.434, 'segs': [{'end': 7596.271, 'src': 'embed', 'start': 7568.737, 'weight': 4, 'content': [{'end': 7576.625, 'text': 'if you want to go for visualization because right now what we are seeing is we have lot of data here, we have lot of data here.', 'start': 7568.737, 'duration': 7.888}, {'end': 7582.327, 'text': 'it shows me there are these tables or data sets which we have worked on.', 'start': 7576.625, 'duration': 5.702}, {'end': 7587.989, 'text': 'that shows me here in the models, but there is no relationship with them.', 'start': 7582.327, 'duration': 5.662}, {'end': 7596.271, 'text': "if you go into visualization, then you don't have any option or you have not created any visualizations based on this data.", 'start': 7587.989, 'duration': 8.282}], 'summary': 'Lack of visualizations for large datasets, no relationships shown.', 'duration': 27.534, 'max_score': 7568.737, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM7568737.jpg'}, {'end': 7651.649, 'src': 'embed', 'start': 7615.869, 'weight': 2, 'content': [{'end': 7624.715, 'text': 'So I can come back here and what I would be interested in is this data set is fine, but I want to do some grouping.', 'start': 7615.869, 'duration': 8.846}, {'end': 7635.72, 'text': "I want to basically have some selective data in this and for that what i can do is let's go for ship mode.", 'start': 7625.136, 'duration': 10.584}, {'end': 7637.801, 'text': 'now this is something what we have.', 'start': 7635.72, 'duration': 2.081}, {'end': 7642.364, 'text': 'so we are in this data field.', 'start': 7637.801, 'duration': 4.563}, {'end': 7644.625, 'text': 'now we have this transform.', 'start': 7642.364, 'duration': 2.261}, {'end': 7651.649, 'text': "so let's go back to transform again and let's choose our orders.", 'start': 7644.625, 'duration': 7.024}], 'summary': 'The speaker is interested in grouping and analyzing selective data based on ship mode and orders.', 'duration': 35.78, 'max_score': 7615.869, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM7615869.jpg'}, {'end': 7799.852, 'src': 'embed', 'start': 7767.137, 'weight': 3, 'content': [{'end': 7776.1, 'text': 'okay, and here I would want to do a summing based on, say, for example, sales.', 'start': 7767.137, 'duration': 8.963}, {'end': 7782.363, 'text': "So this is what I'm going to use for getting a count of the sales.", 'start': 7776.98, 'duration': 5.383}, {'end': 7785.005, 'text': 'Now, grouping by might be we will change this.', 'start': 7782.563, 'duration': 2.442}, {'end': 7792.589, 'text': 'Instead of sales, we are using a ship mode.', 'start': 7789.788, 'duration': 2.801}, {'end': 7797.172, 'text': "What we can also do is let's go for a segment.", 'start': 7792.909, 'duration': 4.263}, {'end': 7799.852, 'text': 'and that would be valid grouping.', 'start': 7798.311, 'duration': 1.541}], 'summary': 'Summarizing sales data by ship mode and segment for grouping.', 'duration': 32.715, 'max_score': 7767.137, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM7767137.jpg'}, {'end': 7911.73, 'src': 'embed', 'start': 7886.062, 'weight': 0, 'content': [{'end': 7896.704, 'text': 'so just to add to the group by step, what we did was, if you see here i have removed the group by filter, which we just did a couple of minutes back,', 'start': 7886.062, 'duration': 10.642}, {'end': 7905.806, 'text': 'and what i have done is, instead of transforming your complete data set, you can basically create a copy of it.', 'start': 7896.704, 'duration': 9.102}, {'end': 7911.73, 'text': 'so, for example, i can just do a copy and then i can come here and do a paste.', 'start': 7905.806, 'duration': 5.924}], 'summary': 'Demonstrated creating a copy of data set instead of transforming it directly, using copy and paste method.', 'duration': 25.668, 'max_score': 7886.062, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM7886062.jpg'}], 'start': 7568.737, 'title': 'Data visualization and transformation in power bi', 'summary': 'Discusses the visualization and grouping of data, emphasizing the need for creating visualizations based on transformed data and demonstrating the process of grouping data by ship mode and segment to obtain relevant sales information. it also focuses on data transformation, group by operations, and data merging in power bi, highlighting the process of creating a copy of the data set for further analysis and applying various transformations including filter application and data loading.', 'chapters': [{'end': 7857.587, 'start': 7568.737, 'title': 'Data visualization and grouping', 'summary': 'Discusses the process of visualizing and grouping data, emphasizing the need for creating visualizations based on transformed data and demonstrating the process of grouping data by ship mode and segment to obtain relevant sales information.', 'duration': 288.85, 'highlights': ['The chapter discusses the process of visualizing and grouping data It explains the importance of creating visualizations based on transformed data and the process of grouping data by ship mode and segment.', 'Emphasizes the need for creating visualizations based on transformed data It stresses the importance of creating visualizations based on transformed data to effectively analyze and present the information derived from the data.', 'Demonstrates the process of grouping data by ship mode and segment to obtain relevant sales information It demonstrates the process of grouping data by ship mode and segment, and the importance of obtaining relevant sales information through this process.']}, {'end': 8274.434, 'start': 7858.208, 'title': 'Data transformation and grouping', 'summary': 'Focuses on data transformation, group by operations, and data merging in power bi, highlighting the process of creating a copy of the data set for further analysis and applying various transformations including filter application and data loading.', 'duration': 416.226, 'highlights': ['The speaker demonstrates the process of creating a copy of the complete data set for further analysis. Creating a copy of the complete data set for further analysis.', 'The speaker shows the process of grouping the data based on shipment, segment, and sales, and applying summing based on sales. Grouping the data based on shipment, segment, and sales, and applying summing based on sales.', 'The speaker explains the process of applying transformations such as filtering and data loading on the copied data set. Applying transformations such as filtering and data loading on the copied data set.']}], 'duration': 705.697, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM7568737.jpg', 'highlights': ['Emphasizes the need for creating visualizations based on transformed data It stresses the importance of creating visualizations based on transformed data to effectively analyze and present the information derived from the data.', 'Demonstrates the process of grouping data by ship mode and segment to obtain relevant sales information It demonstrates the process of grouping data by ship mode and segment, and the importance of obtaining relevant sales information through this process.', 'The speaker demonstrates the process of creating a copy of the complete data set for further analysis. Creating a copy of the complete data set for further analysis.', 'The speaker shows the process of grouping the data based on shipment, segment, and sales, and applying summing based on sales. Grouping the data based on shipment, segment, and sales, and applying summing based on sales.', 'The speaker explains the process of applying transformations such as filtering and data loading on the copied data set. Applying transformations such as filtering and data loading on the copied data set.']}, {'end': 9404.473, 'segs': [{'end': 8350.334, 'src': 'embed', 'start': 8322.531, 'weight': 2, 'content': [{'end': 8329.037, 'text': 'order summarized you have returns, you have scientists, but you basically do not have orders anymore.', 'start': 8322.531, 'duration': 6.506}, {'end': 8330.818, 'text': 'so that was not loaded.', 'start': 8329.037, 'duration': 1.781}, {'end': 8332.559, 'text': 'so i only have this one.', 'start': 8330.818, 'duration': 1.741}, {'end': 8338.504, 'text': 'i only have this one and you have returns.', 'start': 8332.559, 'duration': 5.945}, {'end': 8343.549, 'text': "you have scientists, you don't have the orders column right now.", 'start': 8338.504, 'duration': 5.045}, {'end': 8350.334, 'text': 'that particular data set was not loaded at all because we did not choose that to be loaded.', 'start': 8343.549, 'duration': 6.785}], 'summary': 'Data set missing orders column, only have returns and scientists.', 'duration': 27.803, 'max_score': 8322.531, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM8322531.jpg'}, {'end': 8558.558, 'src': 'embed', 'start': 8505.184, 'weight': 0, 'content': [{'end': 8514.548, 'text': 'If you just place your cursor here, it tells me that we have a relationship between order ID of returns and order ID of orders,', 'start': 8505.184, 'duration': 9.364}, {'end': 8517.149, 'text': 'and that basically allows me to join the data.', 'start': 8514.548, 'duration': 2.601}, {'end': 8519.03, 'text': 'bring it in my one report.', 'start': 8517.149, 'duration': 1.881}, {'end': 8526.513, 'text': 'Now this is basically your data sources you can look at and if you click on your visualization, so that shows you your report.', 'start': 8519.53, 'duration': 6.983}, {'end': 8529.174, 'text': 'Now what we can also do is we can make it interesting.', 'start': 8526.553, 'duration': 2.621}, {'end': 8540.48, 'text': 'now we would not want to scroll through the fields to see wherever or what was the order or what was the order id which was returned.', 'start': 8529.614, 'duration': 10.866}, {'end': 8542.241, 'text': 'now i can do a sorting.', 'start': 8540.48, 'duration': 1.761}, {'end': 8543.461, 'text': 'i can filter out.', 'start': 8542.241, 'duration': 1.22}, {'end': 8551.606, 'text': 'what i can also do is i can use this option which shows slicer here and that basically allows me to work with this data.', 'start': 8543.461, 'duration': 8.145}, {'end': 8557.637, 'text': 'so we have this report here and basically, as i said, you can click in here.', 'start': 8551.606, 'duration': 6.031}, {'end': 8558.558, 'text': 'it shows the data.', 'start': 8557.637, 'duration': 0.921}], 'summary': 'Relationship between order id of returns and order id of orders allows for data joining and visualization.', 'duration': 53.374, 'max_score': 8505.184, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM8505184.jpg'}, {'end': 8777.695, 'src': 'embed', 'start': 8740.326, 'weight': 6, 'content': [{'end': 8742.689, 'text': 'Now I can also basically select yes.', 'start': 8740.326, 'duration': 2.363}, {'end': 8747.549, 'text': 'and that shows me which were the orders which were basically returned.', 'start': 8743.325, 'duration': 4.224}, {'end': 8756.417, 'text': 'So that gives me a quick overview of state-wise what is the geographical location if the orders were returned?', 'start': 8747.709, 'duration': 8.708}, {'end': 8760.961, 'text': 'or maybe I can just click on blank and it shows me the non-returned orders?', 'start': 8756.417, 'duration': 4.544}, {'end': 8767.486, 'text': 'I can again go here and uncheck the GeoGeo option and that shows me all the states.', 'start': 8761.681, 'duration': 5.805}, {'end': 8777.695, 'text': 'Now, once you have done this, this looks like a comprehensive report which can be useful for the viewers, for your management team and so on.', 'start': 8767.686, 'duration': 10.009}], 'summary': 'Interactive report provides state-wise overview of returned and non-returned orders, useful for management.', 'duration': 37.369, 'max_score': 8740.326, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM8740326.jpg'}, {'end': 8917.382, 'src': 'embed', 'start': 8852.218, 'weight': 7, 'content': [{'end': 8858.123, 'text': 'Or it might be what I can do is I can try saving it as a different report.', 'start': 8852.218, 'duration': 5.905}, {'end': 8860.225, 'text': "So let's say save as.", 'start': 8858.684, 'duration': 1.541}, {'end': 8869.873, 'text': 'And now I will basically say additional filters.', 'start': 8861.686, 'duration': 8.187}, {'end': 8873.096, 'text': "OK, let's save it.", 'start': 8871.995, 'duration': 1.101}, {'end': 8881.084, 'text': "So now you see the name on the top, changes to additional filters and now it's saved to publish this.", 'start': 8874.522, 'duration': 6.562}, {'end': 8889.606, 'text': 'and now I can go ahead and publish it, select your workspace and basically it says this is the report being published,', 'start': 8881.084, 'duration': 8.522}, {'end': 8900.409, 'text': 'with our additional filters for the map, which gives a geographical area showing us the information, and then basically, what I can do is,', 'start': 8889.606, 'duration': 10.803}, {'end': 8905.896, 'text': 'once it is done, I can look into my Power BI service, like we did earlier.', 'start': 8900.409, 'duration': 5.487}, {'end': 8907.378, 'text': 'You can do a filtering.', 'start': 8905.956, 'duration': 1.422}, {'end': 8910.283, 'text': 'We can basically query this data.', 'start': 8908.079, 'duration': 2.204}, {'end': 8915.631, 'text': 'We can share it with other users who might be interested in looking at this particular data.', 'start': 8910.683, 'duration': 4.948}, {'end': 8917.382, 'text': 'Now this says it is done.', 'start': 8916.141, 'duration': 1.241}], 'summary': 'Saving and publishing a report with additional filters in power bi, enabling geographical data visualization and sharing capabilities.', 'duration': 65.164, 'max_score': 8852.218, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM8852218.jpg'}], 'start': 8274.434, 'title': 'Working with power bi data', 'summary': 'Discusses applying changes, disabling load, data transformations, creating relationships, using visualizations, publishing reports, analyzing impact, and report functionalities in power bi.', 'chapters': [{'end': 8320.433, 'start': 8274.434, 'title': 'Applying changes and disabling load', 'summary': 'Covers applying changes to orders or summary, and disabling load to remove a table from the report, impacting visuals using its columns, with a focus on loading data based on the changes.', 'duration': 45.999, 'highlights': ['Disabling load removes the table from the report and impacts visuals using its columns.', 'Applying changes will apply all the changes made in orders or summary.', 'Loading data based on the changes is the next step after applying changes.']}, {'end': 8767.486, 'start': 8322.531, 'title': 'Working with data in power bi', 'summary': 'Discusses working with data in power bi, including data transformations, creating relationships between data sets, and using visualizations such as slicers and maps to analyze and filter data.', 'duration': 444.955, 'highlights': ['Data Transformations and Aggregations The chapter emphasizes the ability to perform data transformations and aggregations in Power BI, enabling users to filter, transform, group, and load data, providing flexibility in working with data.', 'Creating Relationships between Data Sets The importance of creating relationships between different data sets in Power BI is highlighted, enabling merging of data from multiple sources for comprehensive analysis and visualization.', 'Utilizing Visualizations like Slicers and Maps The usage of visualizations such as slicers and maps to analyze and filter data in Power BI is emphasized, providing users with interactive tools to explore and present data effectively.']}, {'end': 9404.473, 'start': 8767.686, 'title': 'Power bi report publishing and analysis', 'summary': "Discusses the process of publishing a report in power bi, analyzing its impact, gathering quick insights, and using the 'ask a question' feature to add visuals, offering a comprehensive overview of the report's capabilities and functionalities.", 'duration': 636.787, 'highlights': ['The process of publishing a report in Power BI and analyzing its impact is detailed, including the option to replace or view impact, and the visualization of the impact analysis on different workspaces and reports. Option to replace or view impact when publishing a report, visualization of impact analysis on workspaces and reports.', 'The chapter demonstrates the generation of quick insights by Power BI, showcasing various gathered insights such as sales by ship mode, profit by city or state, average shipping cost by subcategories, and more, offering a valuable tool for data analysis. Demonstration of quick insights generation, insights on sales, profit, shipping cost by subcategories, correlation analysis, count of region and returns.', "The 'Ask a question' feature is highlighted, showcasing the option to ask questions about the data and quickly add visuals to the report, providing a user-friendly and interactive data analysis experience. Demonstration of 'Ask a question' feature, quick addition of visuals to the report based on the questions asked."]}], 'duration': 1130.039, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM8274433.jpg', 'highlights': ['Utilizing Visualizations like Slicers and Maps The usage of visualizations such as slicers and maps to analyze and filter data in Power BI is emphasized, providing users with interactive tools to explore and present data effectively.', 'Creating Relationships between Data Sets The importance of creating relationships between different data sets in Power BI is highlighted, enabling merging of data from multiple sources for comprehensive analysis and visualization.', 'The process of publishing a report in Power BI and analyzing its impact is detailed, including the option to replace or view impact, and the visualization of the impact analysis on different workspaces and reports. Option to replace or view impact when publishing a report, visualization of impact analysis on workspaces and reports.', 'The chapter demonstrates the generation of quick insights by Power BI, showcasing various gathered insights such as sales by ship mode, profit by city or state, average shipping cost by subcategories, and more, offering a valuable tool for data analysis. Demonstration of quick insights generation, insights on sales, profit, shipping cost by subcategories, correlation analysis, count of region and returns.', "The 'Ask a question' feature is highlighted, showcasing the option to ask questions about the data and quickly add visuals to the report, providing a user-friendly and interactive data analysis experience. Demonstration of 'Ask a question' feature, quick addition of visuals to the report based on the questions asked.", 'Disabling load removes the table from the report and impacts visuals using its columns.', 'Applying changes will apply all the changes made in orders or summary.', 'Loading data based on the changes is the next step after applying changes.', 'Data Transformations and Aggregations The chapter emphasizes the ability to perform data transformations and aggregations in Power BI, enabling users to filter, transform, group, and load data, providing flexibility in working with data.']}, {'end': 10524.072, 'segs': [{'end': 9565.301, 'src': 'embed', 'start': 9534.7, 'weight': 6, 'content': [{'end': 9542.582, 'text': 'And then you can say the product quality, high quality, low quality, and probably the price of the product.', 'start': 9534.7, 'duration': 7.882}, {'end': 9545.124, 'text': 'So this is one more dimension table.', 'start': 9543.142, 'duration': 1.982}, {'end': 9552.129, 'text': 'Now you could have another dimension tables which could have a link to this table.', 'start': 9545.624, 'duration': 6.505}, {'end': 9565.301, 'text': 'So when we talk about your schema and if we want to understand data modeling, so there is what we call as your snowflake schema and your star schema.', 'start': 9552.509, 'duration': 12.792}], 'summary': 'Data modeling involves snowflake and star schemas with dimension tables.', 'duration': 30.601, 'max_score': 9534.7, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM9534700.jpg'}, {'end': 9701.444, 'src': 'embed', 'start': 9672.746, 'weight': 0, 'content': [{'end': 9682.353, 'text': 'so you could have product id and location id and same product and location id, but might be that belongs to a different customer id,', 'start': 9672.746, 'duration': 9.607}, {'end': 9684.254, 'text': 'so you can have different entries.', 'start': 9682.353, 'duration': 1.901}, {'end': 9686.576, 'text': "so that's your star schema.", 'start': 9684.254, 'duration': 2.322}, {'end': 9691.939, 'text': 'now if you talk about the other kind of schema, then that is your snowflake.', 'start': 9686.576, 'duration': 5.363}, {'end': 9701.444, 'text': 'you have another dimension table but that is not having a direct connection with fact table.', 'start': 9695.241, 'duration': 6.203}], 'summary': 'Data can be organized in star or snowflake schema with different connections and entries.', 'duration': 28.698, 'max_score': 9672.746, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM9672746.jpg'}, {'end': 9820.673, 'src': 'embed', 'start': 9793.086, 'weight': 1, 'content': [{'end': 9803.217, 'text': 'we may want to connect them so that we can access the data from both of them, and then we can basically have a consolidated information.', 'start': 9793.086, 'duration': 10.131}, {'end': 9807.123, 'text': "So what we can do here is let's look at an example.", 'start': 9803.638, 'duration': 3.485}, {'end': 9811.69, 'text': "so, other than our regular data, let's load some interesting data.", 'start': 9808.109, 'duration': 3.581}, {'end': 9820.673, 'text': 'now. here i have a scenario where i was using a different tool, but then we are investigating or we are doing some threat hunting.', 'start': 9811.69, 'duration': 8.983}], 'summary': 'Connect and access data from different sources for consolidated information.', 'duration': 27.587, 'max_score': 9793.086, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM9793086.jpg'}, {'end': 9975.918, 'src': 'embed', 'start': 9949.222, 'weight': 2, 'content': [{'end': 9954.165, 'text': "I can choose all files and let's go for the first one just to play around with this one.", 'start': 9949.222, 'duration': 4.943}, {'end': 9955.946, 'text': "let's see what does this contain.", 'start': 9954.165, 'duration': 1.781}, {'end': 9958.987, 'text': 'now, this is a huge data set, FortiGate event.', 'start': 9955.946, 'duration': 3.041}, {'end': 9961.729, 'text': "that's mainly for firewall related data.", 'start': 9958.987, 'duration': 2.742}, {'end': 9964.09, 'text': "so let's open this now.", 'start': 9961.729, 'duration': 2.361}, {'end': 9966.092, 'text': 'obviously the data is huge.', 'start': 9964.09, 'duration': 2.002}, {'end': 9970.134, 'text': 'it shows whatever we have based on first 200 rows, which we see.', 'start': 9966.092, 'duration': 4.042}, {'end': 9974.077, 'text': 'there is a serial number, there is a time source.', 'start': 9970.134, 'duration': 3.943}, {'end': 9975.918, 'text': 'uh, what is the protocol which was used?', 'start': 9974.077, 'duration': 1.841}], 'summary': 'Analyzing a large fortigate event dataset, examining first 200 rows for firewall-related data.', 'duration': 26.696, 'max_score': 9949.222, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM9949222.jpg'}, {'end': 10172.556, 'src': 'embed', 'start': 10149.026, 'weight': 4, 'content': [{'end': 10157.011, 'text': "so let's select this and this, And what I have noticed or I've seen already, that the time, what you have here and the time,", 'start': 10149.026, 'duration': 7.985}, {'end': 10159.554, 'text': 'what you see here as the event time is the same.', 'start': 10157.011, 'duration': 2.543}, {'end': 10162.857, 'text': 'So let me go ahead and remove these columns too.', 'start': 10159.994, 'duration': 2.863}, {'end': 10164.019, 'text': "So that's also gone.", 'start': 10163.018, 'duration': 1.001}, {'end': 10165.801, 'text': 'Now we have the date.', 'start': 10164.559, 'duration': 1.242}, {'end': 10172.556, 'text': 'As the column, so I can remove this one, and what I can do is this one I can rename as date.', 'start': 10166.391, 'duration': 6.165}], 'summary': "Data columns removed, date column renamed as 'date.'", 'duration': 23.53, 'max_score': 10149.026, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM10149026.jpg'}, {'end': 10268.028, 'src': 'embed', 'start': 10238.929, 'weight': 3, 'content': [{'end': 10241.954, 'text': 'What we will also do is it will be easier to check.', 'start': 10238.929, 'duration': 3.025}, {'end': 10244.67, 'text': 'So we can replace this.', 'start': 10242.074, 'duration': 2.596}, {'end': 10248.032, 'text': "so i can say replace values and let's give this pattern.", 'start': 10244.67, 'duration': 3.362}, {'end': 10252.476, 'text': "so i'll say device name equals and i want to just remove that.", 'start': 10248.032, 'duration': 4.444}, {'end': 10256.119, 'text': "so let's say okay, and your device name is gone.", 'start': 10252.476, 'duration': 3.643}, {'end': 10258, 'text': 'similarly, you have device id.', 'start': 10256.119, 'duration': 1.881}, {'end': 10259.241, 'text': "let's do the same thing.", 'start': 10258, 'duration': 1.241}, {'end': 10263.405, 'text': "so first let's rename it to device id.", 'start': 10259.241, 'duration': 4.164}, {'end': 10268.028, 'text': "so that's my device id and here i will replace this.", 'start': 10263.405, 'duration': 4.623}], 'summary': 'Replacing device name and id with new values.', 'duration': 29.099, 'max_score': 10238.929, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM10238929.jpg'}, {'end': 10475.881, 'src': 'embed', 'start': 10424.747, 'weight': 5, 'content': [{'end': 10426.869, 'text': "so let's do a split.", 'start': 10424.747, 'duration': 2.122}, {'end': 10432.518, 'text': "let's go by positions and we have the positions here.", 'start': 10426.869, 'duration': 5.649}, {'end': 10439.202, 'text': "so let's, for example, take position of the characters here.", 'start': 10432.518, 'duration': 6.684}, {'end': 10446.427, 'text': 'so, for example, the first one could be you have 5 and 6, 18.', 'start': 10439.202, 'duration': 7.225}, {'end': 10451.551, 'text': "so let's say 18, and then let's go for other values.", 'start': 10446.427, 'duration': 5.124}, {'end': 10453.532, 'text': "so that's again your.", 'start': 10451.551, 'duration': 1.981}, {'end': 10475.881, 'text': "let's go for, say, 28, and then we have 28 and 7, 35, 36, 36 and 5, 41, 42, and you have four, 46, around 52.", 'start': 10453.532, 'duration': 22.349}], 'summary': 'Analyzing positions yields values like 18, 28, 35, 36, 41, 42, and 46.', 'duration': 51.134, 'max_score': 10424.747, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM10424747.jpg'}], 'start': 9406.776, 'title': 'Data modeling and transformation', 'summary': 'Covers data modeling, relationship definition, and transformation processes, focusing on star and snowflake schemas, connecting dimension tables to fact tables, and data reorganization resulting in 13 columns for easier analysis.', 'chapters': [{'end': 9753.214, 'start': 9406.776, 'title': 'Understanding data modeling in bi', 'summary': 'Explains the concept of data modeling, focusing on the relationships between fact and dimension tables in a star schema, and the differences between star and snowflake schemas.', 'duration': 346.438, 'highlights': ['The chapter emphasizes the relationship between fact and dimension tables in data modeling, illustrating how dimension tables hold specific data such as customer information and product details, while the fact table contains measures like quantity and tax amount. The dimension tables hold specific data such as customer information and product details, while the fact table contains measures like quantity and tax amount.', 'It explains the star schema, describing how it involves direct relationships between dimension tables containing data and the fact table containing measures, and mentions the potential for unique or duplicate values in dimension data and repeated values in the fact table. The star schema involves direct relationships between dimension tables containing data and the fact table containing measures, and it mentions the potential for unique or duplicate values in dimension data and repeated values in the fact table.', 'The chapter contrasts the star schema with the snowflake schema, highlighting that the snowflake schema features dimension tables that do not have a direct connection with the fact table, and provides an example involving a product subcategory table with foreign keys. The snowflake schema features dimension tables that do not have a direct connection with the fact table, and provides an example involving a product subcategory table with foreign keys.']}, {'end': 10070.932, 'start': 9753.214, 'title': 'Data modeling and relationship definition', 'summary': 'Discusses data modeling and relationship definition, emphasizing the importance of connecting dimension tables to fact tables, and showcases an example of loading and transforming data sets from different sources to create a consolidated information, with a focus on analyzing firewall-related data.', 'duration': 317.718, 'highlights': ['The importance of connecting dimension tables to fact tables for pulling out information about a product and a subcategory of that. It explains the significance of connecting the fact table with dimension tables to retrieve specific product-related information, showcasing the necessity of establishing relationships within the data model.', 'Demonstrates the process of loading and transforming data sets from different sources to create consolidated information. It showcases an example of loading and transforming data sets from different sources, emphasizing the objective of creating consolidated information by connecting and analyzing data from multiple sources.', 'Analyzing and transforming firewall-related data, focusing on fields such as serial number, time, source, protocol, and raw events. It details the process of analyzing and transforming firewall-related data, highlighting the fields like serial number, time, source, protocol, and raw events, and emphasizes the importance of data transformation for extracting relevant information.']}, {'end': 10524.072, 'start': 10070.932, 'title': 'Data transformation process', 'summary': 'Details a data transformation process, where columns are reorganized and renamed based on positions and delimiters, resulting in the creation of 13 columns, allowing for easier data analysis and manipulation.', 'duration': 453.14, 'highlights': ['Columns reorganized and renamed based on positions and delimiters The speaker reorganizes and renames columns based on positions and delimiters to create 13 columns, allowing for easier data analysis and manipulation.', 'Transformation process results in creation of 13 columns The data transformation process results in the creation of 13 columns, facilitating easier data analysis and manipulation.', 'Splitting based on positions and delimiters to reorganize data The speaker discusses the process of splitting data based on positions and delimiters to reorganize the information for easier analysis and manipulation.']}], 'duration': 1117.296, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM9406776.jpg', 'highlights': ['The chapter emphasizes the relationship between fact and dimension tables in data modeling, illustrating how dimension tables hold specific data such as customer information and product details, while the fact table contains measures like quantity and tax amount.', 'The importance of connecting dimension tables to fact tables for pulling out information about a product and a subcategory of that.', 'The chapter contrasts the star schema with the snowflake schema, highlighting that the snowflake schema features dimension tables that do not have a direct connection with the fact table, and provides an example involving a product subcategory table with foreign keys.', 'Demonstrates the process of loading and transforming data sets from different sources to create consolidated information.', 'Analyzing and transforming firewall-related data, focusing on fields such as serial number, time, source, protocol, and raw events.', 'Columns reorganized and renamed based on positions and delimiters The speaker reorganizes and renames columns based on positions and delimiters to create 13 columns, allowing for easier data analysis and manipulation.', 'The data transformation process results in the creation of 13 columns, facilitating easier data analysis and manipulation.', 'Splitting based on positions and delimiters to reorganize data The speaker discusses the process of splitting data based on positions and delimiters to reorganize the information for easier analysis and manipulation.', 'It explains the star schema, describing how it involves direct relationships between dimension tables containing data and the fact table containing measures, and mentions the potential for unique or duplicate values in dimension data and repeated values in the fact table.']}, {'end': 11310.376, 'segs': [{'end': 10875.792, 'src': 'embed', 'start': 10842.627, 'weight': 1, 'content': [{'end': 10850.53, 'text': 'Now I do have information about your disk, your bandwidth, not interested, your setup rate.', 'start': 10842.627, 'duration': 7.903}, {'end': 10852.531, 'text': "So let's take this.", 'start': 10851.511, 'duration': 1.02}, {'end': 10853.432, 'text': "Let's take this.", 'start': 10852.611, 'duration': 0.821}, {'end': 10854.412, 'text': "Let's take this.", 'start': 10853.512, 'duration': 0.9}, {'end': 10856.513, 'text': "Let's take disk log rate.", 'start': 10855.033, 'duration': 1.48}, {'end': 10858.834, 'text': 'I will remove these columns.', 'start': 10857.034, 'duration': 1.8}, {'end': 10867.905, 'text': 'Then I have my Other information, message performance statistics, not interested, remove these columns.', 'start': 10859.875, 'duration': 8.03}, {'end': 10871.508, 'text': 'So I have my list of columns.', 'start': 10868.666, 'duration': 2.842}, {'end': 10875.792, 'text': 'Now we do see that the user is root here in most cases.', 'start': 10871.929, 'duration': 3.863}], 'summary': 'Data analysis: removing irrelevant columns, identifying root user as prominent', 'duration': 33.165, 'max_score': 10842.627, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM10842627.jpg'}, {'end': 11044.676, 'src': 'embed', 'start': 10969.285, 'weight': 0, 'content': [{'end': 10970.045, 'text': 'So we will see that.', 'start': 10969.285, 'duration': 0.76}, {'end': 10978.027, 'text': "So we have our data which is getting loaded and that's your CPU which we had.", 'start': 10970.685, 'duration': 7.342}, {'end': 10981.688, 'text': 'We have the total sessions which we are seeing here.', 'start': 10978.647, 'duration': 3.041}, {'end': 10990.359, 'text': 'Okay And we have some systematic information.', 'start': 10987.109, 'duration': 3.25}, {'end': 10992.221, 'text': "Let's also go back here home.", 'start': 10990.419, 'duration': 1.802}, {'end': 10997.806, 'text': 'And what I can do is I can go into transform just a quick run through the data.', 'start': 10992.801, 'duration': 5.005}, {'end': 11004.011, 'text': 'So we have the IPs host log description.', 'start': 10999.007, 'duration': 5.004}, {'end': 11012.896, 'text': 'now these are my raw fields, which talk about the type, which talk about the total and which talk about the messages.', 'start': 11004.011, 'duration': 8.885}, {'end': 11017.819, 'text': "so let's give them some name rather than keeping, so we can call them.", 'start': 11012.896, 'duration': 4.923}, {'end': 11019.38, 'text': 'this is type.', 'start': 11017.819, 'duration': 1.561}, {'end': 11042.294, 'text': "let's rename this and let's call it type 1 info, let's call this one type 2 info and then let's call this one type three info today.", 'start': 11019.38, 'duration': 22.914}, {'end': 11043.435, 'text': 'oh, i removed the column.', 'start': 11042.294, 'duration': 1.141}, {'end': 11044.676, 'text': 'sorry, yeah.', 'start': 11043.435, 'duration': 1.241}], 'summary': 'Data loading and transformation, renaming fields for better clarity.', 'duration': 75.391, 'max_score': 10969.285, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM10969285.jpg'}], 'start': 10524.072, 'title': 'Data transformation, renaming, filtering, and set transformation', 'summary': "Covers data transformation by renaming and replacing values, like renaming 'level' to 'vd' and replacing values in 'log description' and 'interface or action'. it also discusses filtering and transforming a firewall data set, focusing on fields such as count, cpu, messages, sessions, ip addresses, and timestamps, to extract specific information for analysis and visualization.", 'chapters': [{'end': 10612.339, 'start': 10524.072, 'title': 'Data transformation and renaming', 'summary': "Discusses the process of transforming data by renaming and replacing values, such as renaming 'level' to 'vd', and replacing values in 'log description', and 'interface or action'.", 'duration': 88.267, 'highlights': ["The process involves renaming 'level' to 'vd' and then replacing all its values with nothing, providing a way to work with the data before loading it.", "The 'log description' is renamed and its values are replaced, enabling the transformation of information and allowing for better organization and understanding.", "The 'interface or action' contains various values that can be manipulated to suit the desired outcome."]}, {'end': 11310.376, 'start': 10612.439, 'title': 'Data set filtering and transformation', 'summary': 'Explains the process of filtering and transforming a data set from a firewall, focusing on fields such as count, cpu, messages, sessions, ip addresses, and timestamps, to extract specific information for analysis and visualization.', 'duration': 697.937, 'highlights': ['The process of filtering and transforming a data set from a firewall involves focusing on fields such as count, CPU, messages, sessions, IP addresses, and timestamps for analysis and visualization. The speaker explains the process of filtering and transforming a data set from a firewall, focusing on fields such as count, CPU, messages, sessions, IP addresses, and timestamps, to extract specific information for analysis and visualization.', 'Emphasis on specific data of interest, such as total sessions, message values, and IP addresses, with a focus on filtering and selecting relevant columns for analysis. The speaker emphasizes the importance of focusing on specific data of interest, such as total sessions, message values, and IP addresses, and highlights the process of filtering and selecting relevant columns for analysis.', 'Explanation of the process of renaming and transforming raw fields into structured categories, and the significance of data modeling in creating meaningful relationships for visualization and analysis. The speaker explains the process of renaming and transforming raw fields into structured categories, and emphasizes the significance of data modeling in creating meaningful relationships for visualization and analysis.']}], 'duration': 786.304, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM10524072.jpg', 'highlights': ["The process involves renaming 'level' to 'vd' and then replacing all its values with nothing, providing a way to work with the data before loading it.", "The 'log description' is renamed and its values are replaced, enabling the transformation of information and allowing for better organization and understanding.", "The 'interface or action' contains various values that can be manipulated to suit the desired outcome.", 'The process of filtering and transforming a data set from a firewall involves focusing on fields such as count, CPU, messages, sessions, IP addresses, and timestamps for analysis and visualization.', 'Emphasis on specific data of interest, such as total sessions, message values, and IP addresses, with a focus on filtering and selecting relevant columns for analysis.', 'Explanation of the process of renaming and transforming raw fields into structured categories, and the significance of data modeling in creating meaningful relationships for visualization and analysis.']}, {'end': 13551.738, 'segs': [{'end': 11757.436, 'src': 'embed', 'start': 11729.203, 'weight': 4, 'content': [{'end': 11733.987, 'text': 'we can look at the source and what is this kind of information.', 'start': 11729.203, 'duration': 4.784}, {'end': 11745.252, 'text': "so here it tells it's a source, internal source port, what we are seeing, and we have some internal or some other information.", 'start': 11733.987, 'duration': 11.265}, {'end': 11748.453, 'text': 'so there might be various other messages.', 'start': 11745.252, 'duration': 3.201}, {'end': 11752.134, 'text': 'but all of these refers to the same key and value.', 'start': 11748.453, 'duration': 3.681}, {'end': 11754.655, 'text': "so let's rename this.", 'start': 11752.134, 'duration': 2.521}, {'end': 11757.436, 'text': "we'll see how we can use all of this information.", 'start': 11754.655, 'duration': 2.781}], 'summary': 'Analyzing internal source port data to identify key values and messages.', 'duration': 28.233, 'max_score': 11729.203, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM11729203.jpg'}, {'end': 12678.849, 'src': 'embed', 'start': 12634.402, 'weight': 3, 'content': [{'end': 12644.411, 'text': "Let's go in and select from 20 or let's say, let's go in here and this shows me 19.", 'start': 12634.402, 'duration': 10.009}, {'end': 12647.314, 'text': "Let's choose 19 and let's go for.", 'start': 12644.411, 'duration': 2.903}, {'end': 12652.939, 'text': 'Okay That should basically now give me only data, which is 20 and beyond.', 'start': 12648.194, 'duration': 4.745}, {'end': 12663.106, 'text': "and now let's select date and time and let's merge this with a space in between.", 'start': 12654.784, 'duration': 8.322}, {'end': 12674.008, 'text': "so let's call separator as space and let's call it date.", 'start': 12663.106, 'duration': 10.902}, {'end': 12678.849, 'text': "let's say okay and that's merged.", 'start': 12674.008, 'duration': 4.841}], 'summary': 'Selected 19 out of 20 data points and merged with date and time.', 'duration': 44.447, 'max_score': 12634.402, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM12634402.jpg'}, {'end': 13023.782, 'src': 'embed', 'start': 12993.226, 'weight': 0, 'content': [{'end': 12996.848, 'text': 'the order of those we can merge and manage them.', 'start': 12993.226, 'duration': 3.622}, {'end': 12998.468, 'text': 'so this this looks good.', 'start': 12996.848, 'duration': 1.62}, {'end': 13000.149, 'text': "now let's go in here.", 'start': 12998.468, 'duration': 1.681}, {'end': 13001.63, 'text': "let's go to get data.", 'start': 13000.149, 'duration': 1.481}, {'end': 13003.631, 'text': "let's go to excel.", 'start': 13001.63, 'duration': 2.001}, {'end': 13010.835, 'text': "let's choose one more file, and i would be interested in http.", 'start': 13003.631, 'duration': 7.204}, {'end': 13015.918, 'text': 'i can look into this win event log, which looks like a one gigabyte file.', 'start': 13010.835, 'duration': 5.083}, {'end': 13023.782, 'text': "this one looks like a smaller 28 megabytes file, so let's take this one and that's getting loaded here.", 'start': 13015.918, 'duration': 7.864}], 'summary': 'Managing and merging files; selecting a 1gb file and a 28mb file for loading.', 'duration': 30.556, 'max_score': 12993.226, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM12993226.jpg'}, {'end': 13551.738, 'src': 'embed', 'start': 13516.394, 'weight': 1, 'content': [{'end': 13517.874, 'text': 'Replace it with nothing.', 'start': 13516.394, 'duration': 1.48}, {'end': 13520.555, 'text': "Yeah, so now it's working.", 'start': 13517.894, 'duration': 2.661}, {'end': 13523.896, 'text': 'And then you basically do a replace.', 'start': 13521.616, 'duration': 2.28}, {'end': 13528.978, 'text': 'You have to be careful with the case sensitiveness.', 'start': 13524.336, 'duration': 4.642}, {'end': 13531.139, 'text': "And let's replace this.", 'start': 13529.718, 'duration': 1.421}, {'end': 13535.76, 'text': "Oh, doesn't look good.", 'start': 13531.159, 'duration': 4.601}, {'end': 13539.481, 'text': 'So let me again do a replace.', 'start': 13537.12, 'duration': 2.361}, {'end': 13543.142, 'text': 'And I want the semicolon to be gone.', 'start': 13540.321, 'duration': 2.821}, {'end': 13547.575, 'text': 'Yeah, and bytes in.', 'start': 13543.162, 'duration': 4.413}, {'end': 13551.738, 'text': 'similarly, you have bytes out.', 'start': 13547.575, 'duration': 4.163}], 'summary': 'Performed multiple replacements, including removing semicolon and modifying case sensitiveness.', 'duration': 35.344, 'max_score': 13516.394, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM13516394.jpg'}], 'start': 11310.696, 'title': 'Data transformation and analysis in power bi', 'summary': 'Covers the process of transforming and analyzing large datasets in power bi, including techniques for splitting, merging, and replacing data fields. it also delves into data analysis, renaming, filtering, and loading processes, with an emphasis on preserving data integrity and optimizing data models. the dataset size is approximately four gigabytes, and the aim is to improve data processing time and enhance data analysis.', 'chapters': [{'end': 11669.453, 'start': 11310.696, 'title': 'Data transformation in power bi', 'summary': 'Discusses the process of transforming and analyzing a large dataset in power bi, highlighting the steps of splitting, merging, and replacing specific data fields to optimize the dataset for analysis and visualization.', 'duration': 358.757, 'highlights': ['The process of splitting, merging, and replacing specific data fields is discussed, showcasing the capability of Power BI in handling large and structured datasets.', 'The importance of removing unnecessary data fields and merging date and time fields is emphasized, demonstrating the efficiency of the data transformation process.', 'The significance of identifying and categorizing specific data types, such as traffic type and subtype, is highlighted to facilitate easier analysis and visualization of the dataset.']}, {'end': 12002.036, 'start': 11669.453, 'title': 'Data analysis and renaming process', 'summary': 'Discusses the process of analyzing and renaming columns in a dataset, including renaming source and destination ip and port columns, removing unnecessary columns, and handling split values based on positions, with an emphasis on preserving data integrity and completeness.', 'duration': 332.583, 'highlights': ['The process of renaming source and destination IP and port columns is discussed, emphasizing the importance of preserving useful information for analysis. The chapter emphasizes the importance of renaming source and destination IP and port columns to maintain useful information for analysis.', 'The chapter addresses the removal of unnecessary columns to streamline the dataset, ensuring that only relevant information is retained. It discusses the removal of unnecessary columns to streamline the dataset and retain only relevant information.', 'The handling of split values based on positions is highlighted, with a focus on extracting and preserving data integrity and completeness. The chapter emphasizes the handling of split values based on positions, with a focus on preserving data integrity and completeness.']}, {'end': 12511.655, 'start': 12003.657, 'title': 'Data transformation and filtering', 'summary': 'Describes the process of data transformation, filtering, and data size reduction, with a focus on techniques such as merge column, text filter, date filter, and data loading, for a dataset of approximately four gigabytes.', 'duration': 507.998, 'highlights': ['The dataset size is approximately four gigabytes, which may require time for data loading and transformation The dataset size is almost four gigabytes, indicating potential time constraints for data loading and transformation.', 'Techniques such as merge column, text filter, and date filter are utilized to transform and filter the data Various techniques such as merge column, text filter, and date filter are employed for data transformation and filtering.', 'The method of selecting specific dates using text filters and date filters is demonstrated The process of selecting specific dates using text filters and date filters is showcased, enabling the extraction of subset data.']}, {'end': 12904.178, 'start': 12511.655, 'title': 'Data transformation and loading', 'summary': 'Discusses the process of transforming and loading data, filtering data based on date, merging datasets, and creating a data model for efficient data analysis, aiming to improve data processing time and optimize data models for analysis.', 'duration': 392.523, 'highlights': ['The process of transforming and loading data, filtering data based on date, merging datasets, and creating a data model for efficient data analysis. Process of transforming and loading data, filtering data based on date, merging datasets, creating a data model for efficient data analysis', 'Filtering the data to keep only data beyond 820, merging date and time fields, and applying a date filter to include data from 20 onwards. Filtering data beyond 820, merging date and time fields, applying a date filter for data from 20 onwards', 'Selecting and loading different data sets step by step, aiming for faster processing time. Selecting and loading data sets step by step for faster processing time', 'Optimizing data models by joining datasets based on device name, device ID, and IPs, and standardizing column naming conventions. Joining datasets based on device name, device ID, and IPs, standardizing column naming conventions']}, {'end': 13551.738, 'start': 12904.878, 'title': 'Data transformation analysis', 'summary': 'Focuses on transforming firewall and http data, including analyzing various columns and filtering out unnecessary information, and merging and managing columns for effective data analysis.', 'duration': 646.86, 'highlights': ['The chapter discusses transforming firewall and HTTP data, with a focus on analyzing and managing columns for effective data analysis.', 'The transcript provides detailed steps on filtering out unnecessary information and merging and managing columns for effective data analysis.', 'The process involves splitting, merging, and formatting timestamp data to extract relevant information for analysis.', 'The transcript also highlights the process of renaming columns and replacing values to organize and clean the data for analysis.', 'The chapter emphasizes the importance of analyzing and filtering specific data points such as acknowledgement packets and bytes for comprehensive data analysis.']}], 'duration': 2241.042, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM11310696.jpg', 'highlights': ['The dataset size is approximately four gigabytes, indicating potential time constraints for data loading and transformation.', 'Various techniques such as merge column, text filter, and date filter are employed for data transformation and filtering.', 'The process of transforming and loading data, filtering data based on date, merging datasets, creating a data model for efficient data analysis.', 'The importance of removing unnecessary data fields and merging date and time fields is emphasized, demonstrating the efficiency of the data transformation process.', 'The process involves splitting, merging, and formatting timestamp data to extract relevant information for analysis.']}, {'end': 14902.022, 'segs': [{'end': 14191.676, 'src': 'embed', 'start': 14101.775, 'weight': 0, 'content': [{'end': 14108.64, 'text': 'we look at the type of information, what kind of information we have, what is the kind of device id.', 'start': 14101.775, 'duration': 6.865}, {'end': 14112.663, 'text': 'so this is what we are seeing in fgt event.', 'start': 14108.64, 'duration': 4.023}, {'end': 14115.385, 'text': 'now in fgt traffic.', 'start': 14112.663, 'duration': 2.722}, {'end': 14118.153, 'text': 'i have My host name.', 'start': 14115.385, 'duration': 2.768}, {'end': 14125.636, 'text': 'I have the date I have device name device ID and these device names and device IDs.', 'start': 14118.634, 'duration': 7.002}, {'end': 14128.397, 'text': 'If we look at the other one.', 'start': 14125.856, 'duration': 2.541}, {'end': 14131.038, 'text': 'That is FGT event.', 'start': 14129.538, 'duration': 1.5}, {'end': 14133.459, 'text': 'So these would be same.', 'start': 14131.639, 'duration': 1.82}, {'end': 14140.027, 'text': 'and then you have other information which is related to traffic here.', 'start': 14133.459, 'duration': 6.568}, {'end': 14149.053, 'text': 'so you look at the source ip, you look at the type of information and then if you look in stream http, you have packets in, packets out, bytes in,', 'start': 14140.027, 'duration': 9.026}, {'end': 14150.154, 'text': 'bytes out.', 'start': 14149.053, 'duration': 1.101}, {'end': 14156.48, 'text': "so let's basically now create a visualization, taking the information from all these three.", 'start': 14150.154, 'duration': 6.326}, {'end': 14166.212, 'text': 'now we can either explicitly define relationship or, when we are creating the visualization, we can go ahead with the relationship.', 'start': 14156.48, 'duration': 9.732}, {'end': 14173.66, 'text': "so let's go here and let me first choose your stream http.", 'start': 14166.212, 'duration': 7.448}, {'end': 14178.845, 'text': 'so i have the client ip, i have the source and source type.', 'start': 14173.66, 'duration': 5.185}, {'end': 14188.813, 'text': "so what i will do is i will basically say, for example, let's start with your fgt event and traffic data.", 'start': 14178.845, 'duration': 9.968}, {'end': 14191.676, 'text': "so let's have a merger between these.", 'start': 14188.813, 'duration': 2.863}], 'summary': 'Analyzing fgt event and traffic data to create visualization with multiple device ids and source ips.', 'duration': 89.901, 'max_score': 14101.775, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM14101775.jpg'}, {'end': 14280.743, 'src': 'embed', 'start': 14249.899, 'weight': 1, 'content': [{'end': 14259.525, 'text': 'so, for example, let me select type one, bring it here and that tells me what is the kind of information i have.', 'start': 14249.899, 'duration': 9.626}, {'end': 14266.89, 'text': "either it is a count or basically it's an action which we can basically then create some filters to filter out the data.", 'start': 14259.525, 'duration': 7.365}, {'end': 14277.92, 'text': "let's look at type 2 info and that basically gives me type 2 info, which is either talking about cpu or total count.", 'start': 14268.109, 'duration': 9.811}, {'end': 14280.743, 'text': 'so let me just get rid of type 2 info.', 'start': 14277.92, 'duration': 2.823}], 'summary': 'Using type one and type 2 info to filter and analyze data.', 'duration': 30.844, 'max_score': 14249.899, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM14249899.jpg'}, {'end': 14681.52, 'src': 'embed', 'start': 14649.411, 'weight': 2, 'content': [{'end': 14667.463, 'text': 'because it has to basically filter out the host name here and you have the source IP which we chose here.', 'start': 14649.411, 'duration': 18.052}, {'end': 14672.773, 'text': "so let me go back to the relationships, and that's your device name, device name.", 'start': 14667.463, 'duration': 5.31}, {'end': 14675.795, 'text': 'this is your device id, device id.', 'start': 14672.773, 'duration': 3.022}, {'end': 14679.918, 'text': 'this is your source ip and host.', 'start': 14675.795, 'duration': 4.123}, {'end': 14681.52, 'text': "let's go back to visualization.", 'start': 14679.918, 'duration': 1.602}], 'summary': 'Filtering out host name, mapping device and source ip, and visualizing data.', 'duration': 32.109, 'max_score': 14649.411, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM14649411.jpg'}], 'start': 13551.738, 'title': 'Data transformation and visualization', 'summary': 'Covers transforming and merging data for efficient stream processing, date filtering, establishing relationships for data visualization, and creating visualizations using fgt event and traffic data, ultimately enhancing data processing efficiency and analysis.', 'chapters': [{'end': 13754.702, 'start': 13551.738, 'title': 'Data transformation and stream processing', 'summary': 'Discusses the process of transforming and merging data, such as client ips and packet information, to streamline and optimize the data sets for efficient stream processing, and applying changes to the data without reloading entire data sets, ultimately enhancing data processing efficiency.', 'duration': 202.964, 'highlights': ['The process involves transforming and merging data, such as client IPs and packet information, to streamline and optimize the data sets for efficient stream processing.', 'Applying changes to the data without reloading entire data sets, ultimately enhancing data processing efficiency.', 'The chapter also discusses enabling load and applying changes to streamline the data processing.', 'The chapter emphasizes the importance of retaining embedded information during the data transformation process.']}, {'end': 14101.775, 'start': 13756.342, 'title': 'Data transformation and analysis', 'summary': 'Discusses data transformation, focusing on date filtering and merging columns, resulting in successful transformation of three datasets with no current relationship between them, and emphasizes the importance of creating relationships for data visualization.', 'duration': 345.433, 'highlights': ['Successfully transformed three larger datasets: one http-related and two traffic and events datasets. The speaker mentions the successful transformation of three bigger datasets: one http related and two are traffic and events.', 'Emphasizes the importance of creating relationships between datasets for data visualization. The speaker discusses the necessity of creating relationships between datasets for data visualization.', 'Filtered and merged columns to obtain the required date and time format for analysis. The chapter details the process of filtering and merging columns to obtain the required date and time format for analysis.', 'Noted the presence of date fields in different formats across datasets and the need for transformation. The speaker highlights the presence of date fields in different formats across datasets and the need for transformation.', 'Mentioned the total number of sessions as a field of interest in the data analysis. The speaker mentions the total number of sessions as a field of interest in the data analysis.']}, {'end': 14470.887, 'start': 14101.775, 'title': 'Data visualization with fgt event and traffic', 'summary': 'Discusses creating a visualization using fgt event and traffic data, establishing relationships between different data tables, and resolving issues related to visual display in power bi.', 'duration': 369.112, 'highlights': ['Creating a visualization using FGT event and traffic data, including data such as source IP, device ID, source type, and type of information.', 'Establishing relationships between different data tables in Power BI to enable data visualization and analysis.', 'Resolving issues related to visual display in Power BI by managing relationships and defining cardinality for the data tables.']}, {'end': 14902.022, 'start': 14470.887, 'title': 'Establishing data relationships and visualization', 'summary': 'Discusses establishing relationships between data fields like source ip and host, creating visualizations, and formatting data for analysis in a massive dataset.', 'duration': 431.135, 'highlights': ['Establishing relationships between source IP and host to define data relationships in the dataset. The process involves defining a relationship between source IP and host, creating direct and indirect relationships, and understanding the many-to-many relationship symbolized by the star symbol.', 'Creating visualizations by selecting and arranging data fields like date, device ID, device name, source, host, destination IP, and type info. The visualization process includes selecting and arranging data fields, adding filters, and formatting the grid, font size, and column headers for improved data visualization.', 'Applying formatting options such as font color, background color, text size, and alignment to enhance the presentation of data. The formatting options include adjusting font color, background color, text size, alignment, and adding titles for improved data presentation.']}], 'duration': 1350.284, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM13551738.jpg', 'highlights': ['Successfully transformed three larger datasets: one http-related and two traffic and events datasets.', 'Creating a visualization using FGT event and traffic data, including data such as source IP, device ID, source type, and type of information.', 'Establishing relationships between source IP and host to define data relationships in the dataset.', 'Emphasizes the importance of creating relationships between datasets for data visualization.', 'Filtering and merging columns to obtain the required date and time format for analysis.']}, {'end': 17622.386, 'segs': [{'end': 15532.946, 'src': 'embed', 'start': 15507.888, 'weight': 9, 'content': [{'end': 15515.653, 'text': 'so you have a host and then you have source IP, from which the connection would be going to say destination IP.', 'start': 15507.888, 'duration': 7.765}, {'end': 15524.419, 'text': 'so you have destination IP which have multiple fields, and then obviously you have info wherein you can say, for example,', 'start': 15515.653, 'duration': 8.766}, {'end': 15530.443, 'text': 'select one of the fields or just place your cursor here and tells you what kind of information it has.', 'start': 15524.419, 'duration': 6.024}, {'end': 15532.946, 'text': 'it has an action, It has policy ID.', 'start': 15530.443, 'duration': 2.503}], 'summary': 'The transcript discusses the structure of destination ip and the information it contains, including action and policy id.', 'duration': 25.058, 'max_score': 15507.888, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM15507888.jpg'}, {'end': 15645.062, 'src': 'embed', 'start': 15585.255, 'weight': 0, 'content': [{'end': 15590.359, 'text': 'Now what I can do is I can start creating relationships between this one.', 'start': 15585.255, 'duration': 5.104}, {'end': 15596.683, 'text': 'So here I have host and I can have host connected to host here.', 'start': 15590.479, 'duration': 6.204}, {'end': 15602.312, 'text': 'So basically, this has now what does it say?', 'start': 15597.768, 'duration': 4.544}, {'end': 15612.542, 'text': 'this should only be used if it is expected that both the columns do not have unique values, but we know that there is unique value, which is 192, 168,', 'start': 15602.312, 'duration': 10.23}, {'end': 15613.623, 'text': '250 dot one.', 'start': 15612.542, 'duration': 1.081}, {'end': 15614.324, 'text': 'So if I say one to one.', 'start': 15613.644, 'duration': 0.68}, {'end': 15623.757, 'text': "It basically says the cardinality you selected isn't valid for this relationship.", 'start': 15619.815, 'duration': 3.942}, {'end': 15630.121, 'text': 'If I basically go for apply security filter in both the directions, it does not still work.', 'start': 15624.238, 'duration': 5.883}, {'end': 15633.122, 'text': 'And you can look at the cross filter which says single.', 'start': 15630.581, 'duration': 2.541}, {'end': 15636.424, 'text': 'So you can have a relationship between this and this.', 'start': 15633.363, 'duration': 3.061}, {'end': 15645.062, 'text': 'so you can say, but the thing here is, there are various combinations for these particular values,', 'start': 15638.115, 'duration': 6.947}], 'summary': 'Creating relationships and applying filters in data analysis.', 'duration': 59.807, 'max_score': 15585.255, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM15585255.jpg'}, {'end': 16355.066, 'src': 'embed', 'start': 16324.809, 'weight': 7, 'content': [{'end': 16335.471, 'text': 'And if you carefully see it, tells me that there is this destination country, which is Canada, and your destination country is Hong Kong,', 'start': 16324.809, 'duration': 10.662}, {'end': 16337.092, 'text': 'source country, something else.', 'start': 16335.471, 'duration': 1.621}, {'end': 16343.56, 'text': 'so we can be looking for a particular country we can be looking at.', 'start': 16337.757, 'duration': 5.803}, {'end': 16347.682, 'text': 'if you look at the dns apps, what is the app here?', 'start': 16343.56, 'duration': 4.122}, {'end': 16348.963, 'text': 'is it a dns?', 'start': 16347.682, 'duration': 1.281}, {'end': 16351.104, 'text': 'is it a net bios?', 'start': 16348.963, 'duration': 2.141}, {'end': 16353.205, 'text': 'is it something else?', 'start': 16351.104, 'duration': 2.101}, {'end': 16355.066, 'text': 'what kind of risk we are talking about?', 'start': 16353.205, 'duration': 1.861}], 'summary': 'Comparison of destination countries canada and hong kong, discussing dns apps and associated risks.', 'duration': 30.257, 'max_score': 16324.809, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM16324809.jpg'}, {'end': 16754.953, 'src': 'embed', 'start': 16724.873, 'weight': 4, 'content': [{'end': 16734.336, 'text': 'so once this report is generated, based on our these simple data sets, we can continuously improve our data modeling.', 'start': 16724.873, 'duration': 9.463}, {'end': 16737.537, 'text': 'so right now we just have this information.', 'start': 16734.336, 'duration': 3.201}, {'end': 16748.73, 'text': 'but what i can do is i can go for the modeling option here and then i can basically mark as date table.', 'start': 16737.537, 'duration': 11.193}, {'end': 16754.953, 'text': 'i can manage the relationships here, i can go in for a new table, new measure.', 'start': 16748.73, 'duration': 6.223}], 'summary': 'Using the generated report, we can continuously improve data modeling.', 'duration': 30.08, 'max_score': 16724.873, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM16724873.jpg'}, {'end': 16864.325, 'src': 'embed', 'start': 16806.116, 'weight': 2, 'content': [{'end': 16808.557, 'text': 'Might be we can work on bytes data.', 'start': 16806.116, 'duration': 2.441}, {'end': 16810.098, 'text': 'So we can do that.', 'start': 16809.218, 'duration': 0.88}, {'end': 16815.901, 'text': 'We can say basically your quarter to date total, month to date total.', 'start': 16810.578, 'duration': 5.323}, {'end': 16820.044, 'text': 'So you can just go for total for category.', 'start': 16816.442, 'duration': 3.602}, {'end': 16821.325, 'text': 'So filters applied.', 'start': 16820.324, 'duration': 1.001}, {'end': 16825.107, 'text': 'And you can basically go for cat totals.', 'start': 16822.045, 'duration': 3.062}, {'end': 16828.529, 'text': 'You can do some mathematical operations if you are interested in.', 'start': 16825.687, 'duration': 2.842}, {'end': 16831.228, 'text': 'you can concatenate list of values.', 'start': 16829.107, 'duration': 2.121}, {'end': 16840.81, 'text': 'so, for example, we can go for total for category, now calculate the total across all values in a category, applying filters in your report,', 'start': 16831.228, 'duration': 9.582}, {'end': 16845.872, 'text': 'so we can basically say what is the data field you would want to work on.', 'start': 16840.81, 'duration': 5.062}, {'end': 16855.215, 'text': "so, for example, let's say the destination ip.", 'start': 16845.872, 'duration': 9.343}, {'end': 16856.415, 'text': 'so let me add that here.', 'start': 16855.215, 'duration': 1.2}, {'end': 16864.325, 'text': 'Now this one is category over which do you want to calculate the total?', 'start': 16859.844, 'duration': 4.481}], 'summary': 'Working on bytes data, calculating quarter to date total, month to date total, and category totals, applying filters, and performing mathematical operations.', 'duration': 58.209, 'max_score': 16806.116, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM16806116.jpg'}, {'end': 16922.161, 'src': 'embed', 'start': 16893.838, 'weight': 5, 'content': [{'end': 16901.805, 'text': 'right. so fact table has fields or columns which are already in your dimension table.', 'start': 16893.838, 'duration': 7.967}, {'end': 16910.132, 'text': 'so your dimension table has the columns as the primary key and facts table has the columns as the foreign key.', 'start': 16901.805, 'duration': 8.327}, {'end': 16918.918, 'text': "plus your facts table will have measures and basically those measures is what you're trying to calculate which can be added to your table,", 'start': 16910.132, 'duration': 8.786}, {'end': 16922.161, 'text': 'that is as your report, and then you can publish the report.', 'start': 16918.918, 'duration': 3.243}], 'summary': 'Fact table has fields from dimension table, measures for calculations, and can be used to publish a report.', 'duration': 28.323, 'max_score': 16893.838, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM16893838.jpg'}, {'end': 17170.743, 'src': 'embed', 'start': 17143.514, 'weight': 8, 'content': [{'end': 17153.633, 'text': 'so in my visualization, based on the fields which we have selected, I have Basically limited the amount of data which can show me in the report.', 'start': 17143.514, 'duration': 10.119}, {'end': 17159.617, 'text': 'However, when we have a bigger server, we can basically create a report with all the data,', 'start': 17153.753, 'duration': 5.864}, {'end': 17166.561, 'text': 'or the suggestion would be to break the data sets into smaller chunks and then go for reports and visualization.', 'start': 17159.617, 'duration': 6.944}, {'end': 17170.743, 'text': 'Now, this report was published to my BI service.', 'start': 17167.061, 'duration': 3.682}], 'summary': 'By limiting data, report was published to bi service.', 'duration': 27.229, 'max_score': 17143.514, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM17143514.jpg'}, {'end': 17465.871, 'src': 'embed', 'start': 17440.514, 'weight': 3, 'content': [{'end': 17452.642, 'text': 'So it says count of the source ports and basically it gives me that these IPs or which I know these are DNS servers have noticeably more source ports.', 'start': 17440.514, 'duration': 12.128}, {'end': 17455.904, 'text': 'It gives me count of total bytes and count of bytes.', 'start': 17453.302, 'duration': 2.602}, {'end': 17458.786, 'text': 'So this is a correlation between total bytes and bytes.', 'start': 17455.944, 'duration': 2.842}, {'end': 17461.207, 'text': 'It gives me count of info.', 'start': 17459.406, 'duration': 1.801}, {'end': 17465.871, 'text': 'So what kind of information we have on packets in?', 'start': 17461.488, 'duration': 4.383}], 'summary': 'An analysis of dns servers shows a noticeable increase in source ports, with a correlation between total bytes and count of bytes.', 'duration': 25.357, 'max_score': 17440.514, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM17440514.jpg'}], 'start': 14902.022, 'title': 'Data analysis and visualization', 'summary': 'Discusses analyzing data relationships, creating relationships between datasets, and filtering and analyzing data with a focus on specific criteria. it also covers data modeling, visualization, and deriving insights from power bi dashboards.', 'chapters': [{'end': 15403.496, 'start': 14902.022, 'title': 'Data relationship analysis', 'summary': 'Discusses the process of analyzing data relationships, including filtering based on host ips and source types, creating relationships between fields, and managing large data sets to ensure efficient analysis.', 'duration': 501.474, 'highlights': ['Creating relationships between fields based on common values, such as host IPs and source types, to filter and analyze data efficiently. The process involves selecting host IPs and source types to filter data and creating relationships between fields based on common values for efficient analysis.', "Managing large data sets by choosing specific values for analysis and ensuring the connection between host and source IPs for accurate data interpretation. When dealing with larger data sets, it's essential to select specific values for analysis and verify the connection between host and source IPs for accurate data interpretation.", 'Optimizing data analysis by creating and deleting relationships between fields to prevent process slowdown and ensure accurate visual reporting. Optimizing data analysis involves creating and deleting relationships between fields to prevent process slowdown and ensure accurate visual reporting.']}, {'end': 16208.499, 'start': 15404.156, 'title': 'Data set relationships and visualization', 'summary': 'Explains the process of creating relationships between three datasets, fgt event, fgt traffic, and stream http, to pull out specific information for visualization and analysis, focusing on creating relationships based on host, device name, device id, date, source ip, and client ip.', 'duration': 804.343, 'highlights': ['Creating relationships between FGT event, FGT traffic, and Stream HTTP datasets based on host, device name, device id, date, source IP, and client IP to extract specific information for visualization and analysis. The process involves creating relationships between datasets based on host, device name, device id, date, source IP, and client IP to extract specific information for visualization and analysis.', 'Filtering and selecting specific fields such as host, level, source IP, client IP, destination IP, bytes in, bytes out, and alert level for visualization and analysis. The process includes filtering and selecting specific fields such as host, level, source IP, client IP, destination IP, bytes in, bytes out, and alert level for visualization and analysis.', 'Examining specific entries to identify potential issues, such as matching source and client IP, alert level, and bytes in and out for further analysis. The chapter also involves examining specific entries to identify potential issues, such as matching source and client IP, alert level, and bytes in and out for further analysis.']}, {'end': 16704.789, 'start': 16208.499, 'title': 'Filtering and analyzing data', 'summary': 'Covers filtering and analyzing data using specific criteria, such as source ip, destination country, and message levels, to extract relevant information and generate reports with a focus on warning messages and total bytes.', 'duration': 496.29, 'highlights': ['Filtering based on message levels and specific criteria such as source IP and destination country The speaker demonstrates filtering the data based on message levels (e.g., alert, warning), and specific criteria like source IP and destination country, to extract relevant information for analysis.', 'Analyzing total bytes and generating reports focused on warning messages The process involves analyzing the total bytes and generating reports with a focus on warning messages, with a count of 400 instances, showcasing the importance of this specific criteria in data analysis.', 'Utilizing device name and creating visualizations for real-time machine data The speaker emphasizes the importance of device name in creating visualizations for real-time machine data, demonstrating its relevance and impact on data analysis and reporting.']}, {'end': 17114.115, 'start': 16704.789, 'title': 'Data modeling and visualization', 'summary': 'Discusses data modeling and visualization, including creating relationships between different datasets, creating measures and quick measures for calculations, and the process of generating and publishing reports on large datasets using power bi.', 'duration': 409.326, 'highlights': ['The process of creating relationships between different datasets to enable visualization and reporting', 'The creation of measures and quick measures for performing calculations and aggregations on the data', 'The challenges and considerations when working with large datasets for generating and publishing reports']}, {'end': 17622.386, 'start': 17115.196, 'title': 'Power bi dashboard basics', 'summary': 'Explains how to limit data for visualization, create reports with filters in power bi, and derive insights from a published dashboard. it emphasizes the importance of selecting the right amount of data for efficient visualization and provides examples of creating and analyzing reports in power bi. additionally, it highlights the process of data modeling and deriving insights from the dashboard.', 'duration': 507.19, 'highlights': ['The chapter emphasizes the importance of selecting the right amount of data for efficient visualization and provides examples of creating and analyzing reports in Power BI. It explains the impact of data volume on visualization and suggests breaking data into smaller chunks for better visualization. It also demonstrates creating reports with limited data and applying filters in Power BI to derive insights.', 'It highlights the process of data modeling and deriving insights from the dashboard. It explains the process of data modeling in Power BI, including creating relationships, facts tables, and measures. Additionally, it demonstrates deriving insights from the dashboard and analyzing the generated insights.', 'It explains the concept of Power BI dashboard and its components, such as visualizations, reports, and datasets. It provides an overview of Power BI dashboard as a single-page visualization tool generated from reports based on datasets. It further explains the components of a dashboard, including visualizations, tiles, and canvases.']}], 'duration': 2720.364, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM14902022.jpg', 'highlights': ['Creating relationships between FGT event, FGT traffic, and Stream HTTP datasets based on host, device name, device id, date, source IP, and client IP for visualization and analysis.', 'Filtering based on message levels and specific criteria such as source IP and destination country to extract relevant information for analysis.', 'Utilizing device name and creating visualizations for real-time machine data, demonstrating its relevance and impact on data analysis and reporting.', 'The chapter emphasizes the importance of selecting the right amount of data for efficient visualization and provides examples of creating and analyzing reports in Power BI.', 'Analyzing total bytes and generating reports focused on warning messages, showcasing the importance of this specific criteria in data analysis.', 'The process of creating measures and quick measures for performing calculations and aggregations on the data.', 'The process of creating relationships between different datasets to enable visualization and reporting.', 'Examining specific entries to identify potential issues, such as matching source and client IP, alert level, and bytes in and out for further analysis.', 'The challenges and considerations when working with large datasets for generating and publishing reports.', 'Optimizing data analysis by creating and deleting relationships between fields to prevent process slowdown and ensure accurate visual reporting.']}, {'end': 18782.559, 'segs': [{'end': 18205.012, 'src': 'embed', 'start': 18177.83, 'weight': 2, 'content': [{'end': 18182.093, 'text': 'Let me make it as 70.', 'start': 18177.83, 'duration': 4.263}, {'end': 18185.215, 'text': 'First, let us create a new card from the visualizations panel.', 'start': 18182.093, 'duration': 3.122}, {'end': 18189.057, 'text': 'So under visualizations, there is an option to create new card.', 'start': 18185.755, 'duration': 3.302}, {'end': 18190.138, 'text': "I'll click on it.", 'start': 18189.478, 'duration': 0.66}, {'end': 18191.719, 'text': 'Let me resize.', 'start': 18190.978, 'duration': 0.741}, {'end': 18194.581, 'text': "I'll move it to the top.", 'start': 18193.78, 'duration': 0.801}, {'end': 18200.35, 'text': "We'll see the total amount of sales or the total revenue that was generated from the sales.", 'start': 18196.087, 'duration': 4.263}, {'end': 18205.012, 'text': "After selecting the card, I'll choose Sales onto Fields.", 'start': 18201.07, 'duration': 3.942}], 'summary': 'Create new card from visualizations panel to display total sales revenue.', 'duration': 27.182, 'max_score': 18177.83, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM18177830.jpg'}, {'end': 18436.724, 'src': 'embed', 'start': 18376.454, 'weight': 1, 'content': [{'end': 18383.942, 'text': "Scroll down, I'll center align it and increase the text size to 15.", 'start': 18376.454, 'duration': 7.488}, {'end': 18388.187, 'text': 'Now if you want, you can also change the date colors to blue.', 'start': 18383.942, 'duration': 4.245}, {'end': 18392.912, 'text': 'So under date inputs, you can go to font color and select any blue color.', 'start': 18388.467, 'duration': 4.445}, {'end': 18402.875, 'text': "Now let's create a line and stacked column chart to visualize sales and profit by each year, each month, and each quarter.", 'start': 18396.454, 'duration': 6.421}, {'end': 18408.316, 'text': 'So from the visualizations tab, let me select line and stacked column chart.', 'start': 18403.555, 'duration': 4.761}, {'end': 18410.977, 'text': "I'll resize this.", 'start': 18410.157, 'duration': 0.82}, {'end': 18419.779, 'text': 'After that, let me add the order date column onto the shared axis.', 'start': 18416.098, 'duration': 3.681}, {'end': 18425.36, 'text': "We'll drag the sales onto column values.", 'start': 18422.839, 'duration': 2.521}, {'end': 18430.96, 'text': "And under line values, I'll take the profit column.", 'start': 18427.638, 'duration': 3.322}, {'end': 18436.724, 'text': 'So here you can see, we have the sales and profit by each year.', 'start': 18433.362, 'duration': 3.362}], 'summary': 'Visualize sales and profit data in line and stacked column chart.', 'duration': 60.27, 'max_score': 18376.454, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM18376454.jpg'}, {'end': 18585.141, 'src': 'embed', 'start': 18546.656, 'weight': 4, 'content': [{'end': 18550.699, 'text': 'And similarly, you can see day 29 gave us the lowest amount of sales.', 'start': 18546.656, 'duration': 4.043}, {'end': 18557.744, 'text': 'Now you can also go to the format tab and switch on the data labels.', 'start': 18552.82, 'duration': 4.924}, {'end': 18564.697, 'text': 'So you can see here in 2014, we got nearly 49.5K profit and nearly 0.48 million dollar sales.', 'start': 18559.192, 'duration': 5.505}, {'end': 18569.261, 'text': 'Similarly, in 2015, we had nearly 61.6K profit and 0.47 million sales and so on.', 'start': 18564.797, 'duration': 4.464}, {'end': 18585.141, 'text': 'Now let me go ahead to the format tab and switch on the border.', 'start': 18579.977, 'duration': 5.164}], 'summary': 'Day 29 had lowest sales. 2014: 49.5k profit, 0.48m sales. 2015: 61.6k profit, 0.47m sales.', 'duration': 38.485, 'max_score': 18546.656, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM18546656.jpg'}, {'end': 18644.695, 'src': 'embed', 'start': 18612.033, 'weight': 0, 'content': [{'end': 18615.575, 'text': 'So you can see here the sales that were made under each quarter for each year.', 'start': 18612.033, 'duration': 3.542}, {'end': 18625, 'text': 'This picture clearly says quarter 4 for every year made the highest amount of sales and quarter 1 made the lowest.', 'start': 18616.816, 'duration': 8.184}, {'end': 18628.322, 'text': 'Let me go to the format tab.', 'start': 18626.861, 'duration': 1.461}, {'end': 18632.328, 'text': "And I'll go to the data colors.", 'start': 18629.907, 'duration': 2.421}, {'end': 18635.31, 'text': "Let's change the colors for each of these quarters.", 'start': 18632.589, 'duration': 2.721}, {'end': 18637.872, 'text': "We'll be using hexadecimal values.", 'start': 18635.79, 'duration': 2.082}, {'end': 18644.695, 'text': 'So under custom colors for quarter one, let me select hexadecimal value as follows.', 'start': 18638.272, 'duration': 6.423}], 'summary': 'Quarter 4 had highest sales, quarter 1 had lowest.', 'duration': 32.662, 'max_score': 18612.033, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM18612033.jpg'}], 'start': 17622.626, 'title': 'Superstore data analysis and power bi dashboard creation', 'summary': 'Details the analysis of superstore sample data, focusing on product names, sales quantity, discount, and profit. it also covers creating a sales dashboard in power bi, including steps such as merging columns, formatting currency values, and using drill-down features to analyze sales and profit.', 'chapters': [{'end': 17655.004, 'start': 17622.626, 'title': 'Superstore data analysis', 'summary': 'Details the analysis of superstore sample data, comprising three tabs and multiple columns, with a report to be prepared and focusing on product names, sales quantity, discount, and profit.', 'duration': 32.378, 'highlights': ['The Superstore sample data analysis involves a three-page report and includes multiple columns such as order date, shipment date, mode of shipment, customer details, and product information.', 'The report will focus on product names, sales quantity, discount, and profit, providing a comprehensive analysis of the Superstore sample data set.', 'The data set is in the form of an Excel file and comprises three tabs, indicating the complexity and depth of the data to be analyzed.']}, {'end': 17993.39, 'start': 17655.304, 'title': 'Creating dashboard in power bi', 'summary': 'Covers the process of extracting and formatting data in power bi desktop, including extracting year, month, and quarter values from the order date column and calculating the total number of days it took for the shipment of the product using power bi functions.', 'duration': 338.086, 'highlights': ['Extracting year, month, and quarter values from the Order Date column The process involves extracting year, month, and quarter values from the Order Date column using Power BI functions.', 'Calculating the total number of days it took for the shipment of the product Using the DATEDIFF function in Power BI to calculate the total number of days it took for the shipment of the product.', 'Formatting the order date and the shipment date Changing the format of the order date and the shipment date to the required format using Power BI modeling options.', 'Creating new columns for day of week and weekday/weekend classification Demonstrating the creation of new columns for day of week and weekday/weekend classification using Power BI functions and IF clauses.', 'Loading dataset onto Power BI Desktop and making changes to the Superstore dataset The process of loading the dataset onto Power BI Desktop and making necessary changes to the Superstore dataset.']}, {'end': 18782.559, 'start': 17993.751, 'title': 'Power bi sales dashboard setup', 'summary': 'Demonstrates the process of creating a sales dashboard in power bi, including steps such as merging columns, formatting currency values, creating new cards for sales and profit, adding slicers and charts, and using drill-down features to analyze sales and profit by year, quarter, month, and day.', 'duration': 788.808, 'highlights': ['The process of merging first name and last name columns to create a full name column is demonstrated, resulting in the creation of a new column named full name containing concatenated first name and last name data. The full name column is created by concatenating the first name and last name columns, resulting in a single column containing the merged data.', 'The sales column is formatted to represent currency values using the $USD symbol, and the profit column is standardized to show two decimal places. The sales column is represented in $USD currency, and the profit column is standardized to display values with two decimal places.', 'The process of creating new cards for displaying total sales, total quantity sold, and total profit is demonstrated, providing insights into the generated revenue and units sold. New cards are created to display the total sales, total quantity sold, and total profit, offering insights into the revenue and units sold.', 'The use of slicers to filter data by order date and the creation of a line and stacked column chart to visualize sales and profit by each year, month, and quarter is showcased, providing a comprehensive view of sales and profit trends over time. Slicers are utilized to filter data by order date, and a line and stacked column chart is created to visualize sales and profit trends by year, month, and quarter, offering a comprehensive view of the data over time.', 'The demonstration of drill-down features to analyze sales and profit by year, quarter, month, and day provides detailed insights into sales and profit performance at different levels of granularity. Drill-down features are showcased to analyze sales and profit performance at various levels, including year, quarter, month, and day, offering detailed insights into the data.', 'The utilization of field map to analyze sales by state and the application of color formatting to visualize sales data on a map is illustrated, enhancing the understanding of sales distribution across different states. The field map is used to analyze sales by state, and color formatting is applied to visualize sales data on a map, enhancing the understanding of sales distribution across states.']}], 'duration': 1159.933, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM17622626.jpg', 'highlights': ['The demonstration of drill-down features to analyze sales and profit by year, quarter, month, and day provides detailed insights into sales and profit performance at different levels of granularity.', 'The utilization of field map to analyze sales by state and the application of color formatting to visualize sales data on a map is illustrated, enhancing the understanding of sales distribution across different states.', 'The process of merging first name and last name columns to create a full name column is demonstrated, resulting in the creation of a new column named full name containing concatenated first name and last name data.', 'The process of creating new cards for displaying total sales, total quantity sold, and total profit is demonstrated, providing insights into the generated revenue and units sold.', 'The use of slicers to filter data by order date and the creation of a line and stacked column chart to visualize sales and profit by each year, month, and quarter is showcased, providing a comprehensive view of sales and profit trends over time.']}, {'end': 20983.828, 'segs': [{'end': 18956.057, 'src': 'embed', 'start': 18931.742, 'weight': 1, 'content': [{'end': 18937.626, 'text': 'So you can see we have a simple pie chart that tells you the profit and the sales that were made by each category.', 'start': 18931.742, 'duration': 5.884}, {'end': 18947.772, 'text': "As it's evident category technology made the highest amount of sales and profit while furniture category made the lowest amount of profit and sales.", 'start': 18938.886, 'duration': 8.886}, {'end': 18949.853, 'text': "I'll go back.", 'start': 18949.293, 'duration': 0.56}, {'end': 18954.156, 'text': "Under format, I'll change the data colors.", 'start': 18951.494, 'duration': 2.662}, {'end': 18956.057, 'text': "Let's go ahead and do that.", 'start': 18954.876, 'duration': 1.181}], 'summary': 'Technology category had highest sales and profit; furniture had lowest.', 'duration': 24.315, 'max_score': 18931.742, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM18931742.jpg'}, {'end': 19044.087, 'src': 'embed', 'start': 18994.666, 'weight': 0, 'content': [{'end': 19002.049, 'text': 'So I can go ahead and rename this tab as overall sales.', 'start': 18994.666, 'duration': 7.383}, {'end': 19008.852, 'text': 'Next, we are going to analyze the sales, profit, quantity sold and all of these.', 'start': 19004.27, 'duration': 4.582}, {'end': 19013.406, 'text': 'by taking region and the state as filters.', 'start': 19010.325, 'duration': 3.081}, {'end': 19015.746, 'text': "So, we'll continue with the same drill.", 'start': 19013.906, 'duration': 1.84}, {'end': 19018.007, 'text': "First, I'll create a text box.", 'start': 19016.126, 'duration': 1.881}, {'end': 19021.848, 'text': "So, under home menu, I'll go to text box.", 'start': 19019.307, 'duration': 2.541}, {'end': 19025.229, 'text': "I'll stretch this.", 'start': 19024.028, 'duration': 1.201}, {'end': 19031.65, 'text': "I'll give a title to this page.", 'start': 19029.37, 'duration': 2.28}, {'end': 19044.087, 'text': 'For example, region and state level analysis.', 'start': 19031.89, 'duration': 12.197}], 'summary': "Renamed tab to 'overall sales', analyzing sales, profit, quantity sold by region and state with a title 'region and state level analysis'", 'duration': 49.421, 'max_score': 18994.666, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM18994666.jpg'}, {'end': 19385.404, 'src': 'embed', 'start': 19349.991, 'weight': 3, 'content': [{'end': 19358.16, 'text': 'For example, if I choose Central as my region, it will give you all the cities that lie in the central region.', 'start': 19349.991, 'duration': 8.169}, {'end': 19364.287, 'text': 'Similarly, you can also search for any state.', 'start': 19360.503, 'duration': 3.784}, {'end': 19366.93, 'text': 'For example, I am taking Minnesota.', 'start': 19364.327, 'duration': 2.603}, {'end': 19376.335, 'text': 'So under Minishota, you can see all the cities and the quantity as well as the profit made.', 'start': 19370.069, 'duration': 6.266}, {'end': 19380.139, 'text': 'Let me now uncheck the filters.', 'start': 19378.317, 'duration': 1.822}, {'end': 19385.404, 'text': 'Now let me go ahead and filter this table in terms of profit.', 'start': 19380.899, 'duration': 4.505}], 'summary': 'The tool allows filtering data by region and state, providing details on cities, quantity, and profit.', 'duration': 35.413, 'max_score': 19349.991, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM19349991.jpg'}, {'end': 19991.802, 'src': 'embed', 'start': 19939.224, 'weight': 2, 'content': [{'end': 19953.813, 'text': "I'll paste that text box here and change the title to say category and sub category level analysis.", 'start': 19939.224, 'duration': 14.589}, {'end': 19970.057, 'text': "First we'll create a slicer where we will take the category as filter.", 'start': 19958.235, 'duration': 11.822}, {'end': 19977.604, 'text': 'so if i want, i can directly copy it from my previous tab and change the fields.', 'start': 19970.057, 'duration': 7.547}, {'end': 19983.368, 'text': 'so let me copy this and paste it here.', 'start': 19977.604, 'duration': 5.764}, {'end': 19983.929, 'text': "i'll sync it.", 'start': 19983.368, 'duration': 0.561}, {'end': 19991.802, 'text': 'let me remove the region field and add category onto the field.', 'start': 19985.635, 'duration': 6.167}], 'summary': 'Creating a slicer for category filter, removing region field, and adding category field.', 'duration': 52.578, 'max_score': 19939.224, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM19939224.jpg'}, {'end': 20289.655, 'src': 'embed', 'start': 20253.696, 'weight': 5, 'content': [{'end': 20257.64, 'text': "So under visualizations, I'll select scatter plot.", 'start': 20253.696, 'duration': 3.944}, {'end': 20258.821, 'text': "I'll resize this.", 'start': 20257.981, 'duration': 0.84}, {'end': 20267.551, 'text': "So I'll add subcategory onto legend.", 'start': 20262.465, 'duration': 5.086}, {'end': 20271.622, 'text': "Then I'll add sales onto the x-axis.", 'start': 20268.618, 'duration': 3.004}, {'end': 20275.307, 'text': 'Quantity sold onto the y-axis.', 'start': 20272.964, 'duration': 2.343}, {'end': 20279.672, 'text': "And under size, we'll take the profit column.", 'start': 20276.528, 'duration': 3.144}, {'end': 20289.655, 'text': 'If I expand this, You can see the sales quantity and the profit that was made for each subcategory of products.', 'start': 20281.514, 'duration': 8.141}], 'summary': 'Created scatter plot with sales on x-axis, quantity sold on y-axis, and profit as size, showing profit made for each subcategory.', 'duration': 35.959, 'max_score': 20253.696, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM20253696.jpg'}, {'end': 20485.161, 'src': 'embed', 'start': 20459.628, 'weight': 6, 'content': [{'end': 20465.531, 'text': 'Likewise, we will now create another line chart that will depict the sales by each year and quarter.', 'start': 20459.628, 'duration': 5.903}, {'end': 20468.173, 'text': 'So, let me copy paste this.', 'start': 20466.352, 'duration': 1.821}, {'end': 20470.955, 'text': 'I will drag this to the right.', 'start': 20468.193, 'duration': 2.762}, {'end': 20476.658, 'text': 'Now I will remove profit from values and add sales.', 'start': 20472.635, 'duration': 4.023}, {'end': 20479.86, 'text': 'Let me uncheck this also.', 'start': 20478.699, 'duration': 1.161}, {'end': 20485.161, 'text': 'So, here you can see we have the sales by each year and quarter.', 'start': 20481.34, 'duration': 3.821}], 'summary': 'Creating a line chart to show sales by year and quarter.', 'duration': 25.533, 'max_score': 20459.628, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM20459628.jpg'}, {'end': 20582.612, 'src': 'embed', 'start': 20545.799, 'weight': 7, 'content': [{'end': 20553.723, 'text': 'So in 2017, quarter 4 made the highest amount of sales while quarter 1 made the lowest amount of sales for chairs.', 'start': 20545.799, 'duration': 7.924}, {'end': 20559.006, 'text': 'Similarly, you can select other subcategories also.', 'start': 20554.784, 'duration': 4.222}, {'end': 20565.909, 'text': 'For example, let me take some other category like machines.', 'start': 20559.226, 'duration': 6.683}, {'end': 20568.731, 'text': "Let's see the profit now.", 'start': 20567.63, 'duration': 1.101}, {'end': 20582.612, 'text': 'So for machines subcategory, you can see in 2014, quarter 4 made the highest amount of profit while quarter 3 made the lowest amount of profit.', 'start': 20570.342, 'duration': 12.27}], 'summary': 'In 2017, q4 had highest chair sales, q1 had lowest. in machines, 2014 q4 had highest profit, q3 lowest.', 'duration': 36.813, 'max_score': 20545.799, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM20545799.jpg'}, {'end': 20838.965, 'src': 'embed', 'start': 20806.311, 'weight': 4, 'content': [{'end': 20809.052, 'text': 'different segments and by categories.', 'start': 20806.311, 'duration': 2.741}, {'end': 20817.576, 'text': 'So one of the important feature that Power BI Service gives you is that you can ask questions about your data and it will tell you what the exact solution is.', 'start': 20809.792, 'duration': 7.784}, {'end': 20822.278, 'text': 'For example, you can see here, you have a field where you can ask questions.', 'start': 20817.976, 'duration': 4.302}, {'end': 20828.123, 'text': 'So for example, if I type total sales, and hit enter.', 'start': 20822.578, 'duration': 5.545}, {'end': 20832.904, 'text': 'See, it tells you the total amount of sales that were made which is 2.3 million dollars.', 'start': 20828.883, 'duration': 4.021}, {'end': 20838.965, 'text': 'Similarly, if I want to find out the total profit, I can search for it here.', 'start': 20833.344, 'duration': 5.621}], 'summary': "Power bi service allows asking questions about data, giving exact solutions. for instance, typing 'total sales' reveals 2.3 million dollars in sales, enhancing data analysis.", 'duration': 32.654, 'max_score': 20806.311, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM20806311.jpg'}, {'end': 20891.871, 'src': 'embed', 'start': 20857.697, 'weight': 8, 'content': [{'end': 20863.34, 'text': 'It gives you a nice line chart where you can see the total profit that was made over all the four years.', 'start': 20857.697, 'duration': 5.643}, {'end': 20875.406, 'text': 'Next, suppose I search for total quantity sold by segment.', 'start': 20863.92, 'duration': 11.486}, {'end': 20891.871, 'text': 'See, it gives me a bar chart where you can see the total quantity that was sold and it gives you a list of options where you can find out the total quantity that was sold by city,', 'start': 20879.248, 'duration': 12.623}], 'summary': 'Visualizes total profit and quantity sold by segment and city.', 'duration': 34.174, 'max_score': 20857.697, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM20857697.jpg'}, {'end': 20970.919, 'src': 'embed', 'start': 20945.866, 'weight': 9, 'content': [{'end': 20952.587, 'text': 'So on the right, you can see Power BI has itself given us some insights from the data and the visualizations we have made.', 'start': 20945.866, 'duration': 6.721}, {'end': 20955.268, 'text': 'You can see sales by year and quarter.', 'start': 20952.607, 'duration': 2.661}, {'end': 20964.675, 'text': 'the sales by day of week and quarter, sales by shipment mode, where you can see the standard class ship mode made the highest amount of sales.', 'start': 20956.57, 'duration': 8.105}, {'end': 20970.919, 'text': 'You can also see the sales and count of city by year and many more.', 'start': 20966.356, 'duration': 4.563}], 'summary': 'Power bi provides insights on sales by year, quarter, day of week, and shipment mode, with standard class ship mode generating the highest sales.', 'duration': 25.053, 'max_score': 20945.866, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM20945866.jpg'}], 'start': 18783.059, 'title': 'Sales and profit analysis', 'summary': 'Covers techniques for sales and profit analysis using various visualizations, including donut and pie charts, slicers, clustered bar chart, and more, aiming to analyze sales, profit, and quantity by different dimensions, such as state, region, category, and subcategory, providing insights for decision-making in business intelligence and promoting specific products for higher profit.', 'chapters': [{'end': 18994.146, 'start': 18783.059, 'title': 'Sales dashboard analysis', 'summary': 'Explains the creation of a sales dashboard using donut and pie charts to analyze sales and profit by state and product category, indicating california had the highest sales and technology category made the highest profit and sales.', 'duration': 211.087, 'highlights': ['The donut chart indicates consumer segment made the highest amount of sales and profit while home office segment made the lowest.', 'California had the highest amount of sales, followed by New York and Texas.', 'Category technology made the highest amount of sales and profit while furniture category made the lowest amount of profit and sales.']}, {'end': 19255.818, 'start': 18994.666, 'title': 'Power bi region and state analysis', 'summary': 'Discusses creating slicers for region, state, and year in power bi to analyze sales, profit, and quantity, along with the feature of using the search option to filter data, aiming to enhance data analysis and visualization.', 'duration': 261.152, 'highlights': ['The chapter discusses creating slicers for region, state, and year in Power BI to analyze sales, profit, and quantity, aiming to enhance data analysis and visualization.', 'The speaker demonstrates the process of formatting the slicers by adjusting font color, background, text size, and border, aiming to improve the visual appeal and usability of the slicers.', 'The feature of using the search option in Power BI slicers is highlighted, allowing users to easily search and filter data, potentially saving time and improving user experience.']}, {'end': 19794.791, 'start': 19256.458, 'title': 'Sales data visualization and analysis', 'summary': 'Demonstrates data visualization techniques using a table, clustered bar chart, and area chart to analyze sales and profit by city, state, region, year, category, and subcategory, showcasing the highest profit-making cities and products as well as the state-wise sales and profit distribution.', 'duration': 538.333, 'highlights': ['The chapter showcases data visualization techniques using a table, clustered bar chart, and area chart to analyze sales and profit by city, state, region, year, category, and subcategory. The demonstration includes techniques such as resizing, formatting, changing font colors, adding borders, and using filters to analyze sales and profit distribution.', 'The demonstration highlights the highest profit-making cities and products, such as New York City, Los Angeles, and Seattle, and the copiers subcategory under the technology category. It showcases the highest profit-making cities and products, providing quantifiable data on the highest profit cities and the highest profit-making subcategory under the technology category.', 'The chapter showcases state-wise sales and profit distribution, enabling the analysis of sales by each state and category for specific years, along with the profit by year and category under different regions. It enables state-wise analysis of sales and profit distribution, including the demonstration of sales and profit distribution for specific years, categories, and regions.']}, {'end': 20289.655, 'start': 19796.312, 'title': 'Sales analysis and visualization', 'summary': 'Covers the analysis of sales and profit by segment, region, and category, utilizing visualizations such as funnel, tree map, pie chart, and scatter plot to provide insights and trends for decision-making in business intelligence.', 'duration': 493.343, 'highlights': ['The tree map visualization depicted that the consumer segment made the highest amount of sales and profit, while home office made the lowest amount of sales and profit. The tree map visualization showed the sales and profit by each region and segment, with the consumer segment having the highest sales and profit, and home office having the lowest.', 'The pie chart illustrated that the consumer segment had the highest sales and quantity sold, while home office had the least quantity sold and sales. The pie chart displayed the quantity and sales by segment, revealing that the consumer segment had the highest sales and quantity sold, while home office had the least.', 'The scatter plot provided insights into the sales quantity and profit for each subcategory of products. The scatter plot visualized the sales quantity and profit for each subcategory of products, providing a comprehensive view of the performance of different product categories.']}, {'end': 20628.606, 'start': 20290.036, 'title': 'Sales and profit analysis', 'summary': 'Discusses the analysis of sales quantity, profit at category and subcategory level, with a focus on promoting products like phones and chairs due to their high profit despite lower quantity sold, and the identification of highest and lowest sales and profit by year and quarter.', 'duration': 338.57, 'highlights': ['Phones and chairs yield high profit with lower quantity sold, suggesting promotion of these products for increased profitability. Phones and chairs have high profit despite lower quantity sold.', 'Analysis of profit by year and quarter reveals highest profit in 2014 Q4, and lowest in 2014 Q2 and 2016 Q1. 2014 Q4 had highest profit, while 2014 Q2 and 2016 Q1 had the lowest.', 'The report includes three analyses: overall sales, region and state level analysis, and sales quantity and profit by category and subcategory. Three reports were created, including overall sales, region and state level analysis, and sales quantity and profit by category and subcategory.']}, {'end': 20983.828, 'start': 20628.606, 'title': 'Publishing report to power bi dashboard', 'summary': 'Details the process of publishing a report onto the power bi dashboard, pinning visualizations, deriving insights, and utilizing features such as asking questions about data, with a successful publishing of a report to power bi dashboard, pinning three tabs onto a new dashboard, and deriving insights from the visualizations.', 'duration': 355.222, 'highlights': ['The report was successfully published onto the Power BI dashboard, and three tabs were created: overall sales, sales by region, and by category and subcategory.', 'Pinning visualizations onto a new dashboard was demonstrated, including pinning charts and graphs, and resizing and arranging them on the dashboard.', 'The Power BI Service provides the feature to ask questions about data and receive exact solutions, such as finding the total amount of sales, total profit, and visualizing data through charts based on different queries.', 'Insights can be derived from the visualizations, such as sales by year and quarter, sales by day of week and quarter, and sales by shipment mode.', 'The session concludes with an invitation for questions and a reminder to subscribe to Simply Learn channel.']}], 'duration': 2200.769, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TBVss5711QM/pics/TBVss5711QM18783059.jpg', 'highlights': ['The donut chart indicates consumer segment made the highest amount of sales and profit while home office segment made the lowest.', 'California had the highest amount of sales, followed by New York and Texas.', 'Category technology made the highest amount of sales and profit while furniture category made the lowest amount of profit and sales.', 'The chapter discusses creating slicers for region, state, and year in Power BI to analyze sales, profit, and quantity, aiming to enhance data analysis and visualization.', 'The chapter showcases data visualization techniques using a table, clustered bar chart, and area chart to analyze sales and profit by city, state, region, year, category, and subcategory.', 'The tree map visualization depicted that the consumer segment made the highest amount of sales and profit, while home office made the lowest amount of sales and profit.', 'Phones and chairs yield high profit with lower quantity sold, suggesting promotion of these products for increased profitability.', 'Analysis of profit by year and quarter reveals highest profit in 2014 Q4, and lowest in 2014 Q2 and 2016 Q1.', 'The report was successfully published onto the Power BI dashboard, and three tabs were created: overall sales, sales by region, and by category and subcategory.', 'The Power BI Service provides the feature to ask questions about data and receive exact solutions, such as finding the total amount of sales, total profit, and visualizing data through charts based on different queries.']}], 'highlights': ['Power BI is a leader in the Gartner 2021 Magic Quadrant report for analytics and business intelligence platforms', 'Power BI allows access to vast volumes of data from multiple sources such as Excel, CSV, XML, JSON, and PDF', 'Power BI supports real-time stream analytics, fetching data from multiple sensors and social media sources', 'Power BI provides support for multiple data sources, including Excel, CSV, SQL Server, and web files', 'Power BI offers custom visualization, providing access to a custom library of visualizations', 'Tableau can handle large volumes of data easily and provides extensive features for visualizing the data', 'Power BI is way less expensive than Tableau, with the Professional version costing less than $10 per month per user', 'The course covers a range of topics including Tableau statistics, building interactive dashboards, arithmetic, logical and LOD calculations, and various visualization techniques such as heatmap, waterfall, Pareto, clustering, and forecasting', 'The chapter provides insights into the licensing options for Power BI, including the pricing and limitations of the pro version, and the conditional-based features of the premium account, highlighting the differences in features and limitations between the pro version and the premium account', 'The visuals provide sales information and allow for customization, offering a user-friendly experience', 'Options for sharing the report, including sharing to teams and setting up common pains, are demonstrated', 'The process of creating a report with visualizations, demonstrating the utilization of various visualization options and fields to generate a comprehensive report', 'Importance of making basic transformations before loading large datasets', 'The process involves selecting, transforming, and loading data, and creating aggregations and filters to enable efficient data analysis and decision-making', 'The chapter covers various data transformation tasks including changing data types, using first row as header, replacing values, running merge queries, and performing analytics using split, duplicate, and renaming columns in a dataset', 'Emphasizes the need for creating visualizations based on transformed data It stresses the importance of creating visualizations based on transformed data to effectively analyze and present the information derived from the data', 'Utilizing Visualizations like Slicers and Maps The usage of visualizations such as slicers and maps to analyze and filter data in Power BI is emphasized, providing users with interactive tools to explore and present data effectively', 'The chapter emphasizes the relationship between fact and dimension tables in data modeling, illustrating how dimension tables hold specific data such as customer information and product details, while the fact table contains measures like quantity and tax amount', "The process involves renaming 'level' to 'vd' and then replacing all its values with nothing, providing a way to work with the data before loading it", 'Creating Relationships between Data Sets The importance of creating relationships between different data sets in Power BI is highlighted, enabling merging of data from multiple sources for comprehensive analysis and visualization', 'The dataset size is approximately four gigabytes, indicating potential time constraints for data loading and transformation', 'The demonstration of drill-down features to analyze sales and profit by year, quarter, month, and day provides detailed insights into sales and profit performance at different levels of granularity', 'The donut chart indicates consumer segment made the highest amount of sales and profit while home office segment made the lowest', 'California had the highest amount of sales, followed by New York and Texas', 'Category technology made the highest amount of sales and profit while furniture category made the lowest amount of profit and sales']}