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Data Analyst Interview Questions And Answers | Data Analytics Interview Questions | Simplilearn

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This video,Data Analyst Interview Questions And Answersby Simplilearn, will take you through the most frequently asked Data Analytics Interview Questions. Data analysis is one of the trending jobs of the 21st century. This video covers all the important questions that would help you crack a data analyst interview. It has a set of basic questions related to the data analytics field. It also has a collection of beginner, intermediate and advanced-level questions based on MS Excel, SQL, Tableau, and Python. It would enrich your theoretical and practical knowledge of data analytics. What you will learn
0:00 Data Analyst Interview Questions And Answers
00:34 What is the difference between Data Mining and Data Profiling?
01:38 Define the term Data Wrangling
02:40 What are the common problems that data analysts encounter during analysis?
03:09 What are the various steps involved in any analytics project?
04:03 Which technical tools have you used for analysis and presentation purposes?
04:50 What are the best practices for data cleaning?
05:34 How can you handle missing values in a dataset?
06:54 Define normal Distribution
07:39 What is Time Series analysis?
08:31 How is joining different from blending in Tableau?
09:16 How is overfitting different from underfitting?
10:01 In MS Excel, A numeric Value can be treated as text value if it precedes with
10:29 What is the difference between COUNT, COUNTA, COUNTBLANK and COUNTIF in Excel?
11:29 Explain how VLOOKUP works in Excel?
13:21 How do you subset or filter data in SQL?
15:39 What is the difference between WHERE and HAVING clause in SQL?
17:58 Write the Python code to create an employees dataframe and display the head and summary of it.
18:46 How will you select the Department and Age columns from an Employees dataframe?
19:22 What is the criteria to say whether a developed data model is good or not?
20:08 What is the significance of Exploratory data analysis?
21:09 Explain descriptive, predictive, and prescriptive analytics.
23:01 What are the different types of sampling techniques used by data analysts?
24:45 What are the different types of Hypothesis testing?
25:42 Describe univariate, bivariate, and multivariate analysis.
29:56 write a SQL query to find the record with fourth highest market price
33:36 Create a dual axis chart in Tableau to present Sales
34:36 Design a view in Tableau to show State wise Sales
40:39 How do you write a stored procedure in SQL?
43:01 What is the difference between Tree maps and Heat maps in Tableau
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âžˇď¸ŹAbout Caltech Data Analytics Bootcamp
The Caltech Data Analytics Bootcamp is ideal for working professionals from all backgrounds. Develop core skills such as mastering Excel, creating data-driven presentations, data manipulation with SQL, analyzing data with Python, and data visualization with Tableau. Learn data analysis with tools such as AWS.
âś…Key Features
- Simplilearn Career Service helps you get noticed by top hiring companies
- Data Analytics Bootcamp Certificate from Caltech CTME
- Caltech CTME Circle Membership
- Access to integrated Data Analytics Labs
âś…Skills Covered
- Data Analytics
- Statistical Analysis using Excel
- Data Analytics using Python
- Data Visualization with Tableau
- Linear and Logistic Regression
- Data Manipulation
- & more
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common problems include duplicates, missing values, security, compliance, purging, and storage.', 'duration': 46.791, 'max_score': 134.821, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI134821.jpg'}, {'end': 237.114, 'src': 'embed', 'start': 207.011, 'weight': 2, 'content': [{'end': 210.734, 'text': 'This is probably the most important part of the process.', 'start': 207.011, 'duration': 3.723}, {'end': 214.337, 'text': 'Everything after it falls in and then you can come back to it.', 'start': 211.475, 'duration': 2.862}, {'end': 216.306, 'text': 'Two, data collection.', 'start': 215.106, 'duration': 1.2}, {'end': 218.407, 'text': 'Data cleaning, number three.', 'start': 217.067, 'duration': 1.34}, {'end': 220.748, 'text': 'Four, data exploration analysis.', 'start': 218.807, 'duration': 1.941}, {'end': 223.249, 'text': 'And five, interpret the results.', 'start': 221.228, 'duration': 2.021}, {'end': 227.05, 'text': 'Number five is a close second for being the most important.', 'start': 224.189, 'duration': 2.861}, {'end': 231.432, 'text': "If you can't interpret what you bring to the table to your clients, you're in trouble.", 'start': 227.33, 'duration': 4.102}, {'end': 237.114, 'text': 'So, when this question comes up, you probably want to focus on those two, noting that the rest of it does.', 'start': 232.152, 'duration': 4.962}], 'summary': 'Key steps in the process: data collection, cleaning, exploration, analysis, and result interpretation.', 'duration': 30.103, 'max_score': 207.011, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI207011.jpg'}, {'end': 287.362, 'src': 'embed', 'start': 255.624, 'weight': 5, 'content': [{'end': 257.245, 'text': "There's a wide variety out there.", 'start': 255.624, 'duration': 1.621}, {'end': 266.55, 'text': 'SQL Server, MySQL, you have your Excel, your SPSS, which is the IBM platform, Tableau, Python.', 'start': 257.964, 'duration': 8.586}, {'end': 268.471, 'text': 'You have all these different tools in here.', 'start': 267.11, 'duration': 1.361}, {'end': 273.193, 'text': 'Now, certainly a lot of jobs are going to be narrowed in on just a few of these tools.', 'start': 268.691, 'duration': 4.502}, {'end': 277.316, 'text': "You're not going to have a Microsoft SQL Server or MySQL Server,", 'start': 273.333, 'duration': 3.983}, {'end': 287.362, 'text': 'but you better understand how to do basic SQL polls and also understanding Excel and how the different formats for column and how to get those set up.', 'start': 277.316, 'duration': 10.046}], 'summary': 'A wide variety of tools available, including sql server, mysql, excel, spss, tableau, and python. understanding basic sql and excel is crucial.', 'duration': 31.738, 'max_score': 255.624, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI255624.jpg'}, {'end': 346.342, 'src': 'embed', 'start': 302.999, 'weight': 1, 'content': [{'end': 308.86, 'text': 'Make a data cleaning plan by understanding where the common errors take place and keep communications open.', 'start': 302.999, 'duration': 5.861}, {'end': 312.181, 'text': 'Identify and remove duplicates before working with the data.', 'start': 309.38, 'duration': 2.801}, {'end': 315.142, 'text': 'This will lead to an effective data analysis process.', 'start': 312.641, 'duration': 2.501}, {'end': 317.583, 'text': 'Focus on the accuracy of the data.', 'start': 315.942, 'duration': 1.641}, {'end': 319.464, 'text': 'Maintain the value types of data.', 'start': 317.823, 'duration': 1.641}, {'end': 323.465, 'text': 'Provide mandatory constraints and set cross-field validation.', 'start': 319.604, 'duration': 3.861}, {'end': 331.429, 'text': 'Standardize the data at the point of entry so that it is less chaotic, and you will be able to ensure that all the information is standardized,', 'start': 324.306, 'duration': 7.123}, {'end': 333.21, 'text': 'leading to fewer errors on entry.', 'start': 331.429, 'duration': 1.781}, {'end': 340.397, 'text': 'Number seven, how can you handle missing values in a data set? Listwise deletion.', 'start': 334.352, 'duration': 6.045}, {'end': 346.342, 'text': 'In listwise deletion method, entire record is excluded from analysis if any single value is missing.', 'start': 340.597, 'duration': 5.745}], 'summary': 'Plan for data cleaning to remove duplicates and ensure accuracy and standardization, using listwise deletion for missing values.', 'duration': 43.343, 'max_score': 302.999, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI302999.jpg'}, {'end': 470.816, 'src': 'embed', 'start': 423.26, 'weight': 7, 'content': [{'end': 429.401, 'text': 'Normal distribution is a type of continuous probability distribution that is symmetric about the mean and in the graph,', 'start': 423.26, 'duration': 6.141}, {'end': 432.782, 'text': 'normal distribution will appear as a bell curve.', 'start': 429.401, 'duration': 3.381}, {'end': 436.283, 'text': 'The mean, median, and mode are equal.', 'start': 433.342, 'duration': 2.941}, {'end': 440.684, 'text': "That's a quick way to know if you have normal distribution is you can compute mean, median, and mode.", 'start': 436.523, 'duration': 4.161}, {'end': 443.485, 'text': 'All of them are located at the center of the distribution.', 'start': 441.244, 'duration': 2.241}, {'end': 448.086, 'text': '68% of the data lies within one standard deviation of the mean.', 'start': 443.505, 'duration': 4.581}, {'end': 454.109, 'text': '95% of the data falls within two standard deviations of the mean.', 'start': 449.226, 'duration': 4.883}, {'end': 457.971, 'text': '99.7% of the data lies within three standard deviations of the mean.', 'start': 454.369, 'duration': 3.602}, {'end': 461.931, 'text': 'What is time series analysis?', 'start': 459.269, 'duration': 2.662}, {'end': 470.816, 'text': 'Time series analysis is a statistical method that deals with ordered sequence of values of a variable of equally spaced time intervals.', 'start': 462.751, 'duration': 8.065}], 'summary': 'Normal distribution is symmetric, with mean, median, and mode equal. 68% within 1 standard deviation, 95% within 2, and 99.7% within 3. time series analysis involves statistical analysis of ordered time intervals.', 'duration': 47.556, 'max_score': 423.26, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI423260.jpg'}, {'end': 537.614, 'src': 'embed', 'start': 511.692, 'weight': 8, 'content': [{'end': 518.113, 'text': "How is joining different from blending in Tableau? So now we're going to jump into the Tableau package.", 'start': 511.692, 'duration': 6.421}, {'end': 519.534, 'text': 'Data joining.', 'start': 518.754, 'duration': 0.78}, {'end': 524.174, 'text': 'Data joining can only be done when the data comes from the same source.', 'start': 519.714, 'duration': 4.46}, {'end': 530.236, 'text': 'Combining two tables from the same database or two or more worksheets from the same Excel file.', 'start': 524.855, 'duration': 5.381}, {'end': 535.857, 'text': 'All the combined tables or sheets contains common set of dimensions and measures.', 'start': 530.636, 'duration': 5.221}, {'end': 537.614, 'text': 'Data Blending.', 'start': 536.934, 'duration': 0.68}], 'summary': 'In tableau, data joining requires same source, while blending combines common dimensions and measures.', 'duration': 25.922, 'max_score': 511.692, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI511692.jpg'}, {'end': 590.167, 'src': 'embed', 'start': 562.081, 'weight': 9, 'content': [{'end': 567.026, 'text': 'Overfitting Probably the biggest danger in data analytics today is overfitting.', 'start': 562.081, 'duration': 4.945}, {'end': 570.729, 'text': 'Model trains from the data too well using the training set.', 'start': 567.806, 'duration': 2.923}, {'end': 574.432, 'text': 'The performance drops significantly over the test set.', 'start': 571.63, 'duration': 2.802}, {'end': 579.837, 'text': 'Happens when the model learns the noise and random fluctuations in the training data set in detail.', 'start': 574.893, 'duration': 4.944}, {'end': 585.462, 'text': 'And again, the performance drops way below what the test set has.', 'start': 580.658, 'duration': 4.804}, {'end': 590.167, 'text': 'The model neither trains the data well nor can generalize to new data.', 'start': 586.383, 'duration': 3.784}], 'summary': 'Overfitting is the biggest danger in data analytics, leading to significant performance drops over the test set.', 'duration': 28.086, 'max_score': 562.081, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI562081.jpg'}], 'start': 8.442, 'title': 'Data analytics and best practices', 'summary': 'Covers differences between data mining and profiling, data wrangling, common analysis problems, steps in analytics projects, and best practices for data cleaning, handling missing values, understanding normal distribution, and tableau techniques.', 'chapters': [{'end': 277.316, 'start': 8.442, 'title': 'Data analytics interview insights', 'summary': 'Discusses the differences between data mining and data profiling, the process of data wrangling, common problems encountered during analysis, various steps in an analytics project, and technical tools used for analysis and presentation.', 'duration': 268.874, 'highlights': ['Data wrangling is a process of cleaning, structuring, and enriching the raw data into a desired usable format for better decision making, with 80% of data analytics usually involving this step. Data wrangling involves cleaning, structuring, and enriching raw data for better decision making, with 80% of data analytics usually involving this step.', 'Understanding the problem and interpreting the results are the most important steps in an analytics project. Understanding the problem and interpreting the results are the most important steps in an analytics project.', 'Common problems encountered during analysis include handling duplicate and missing values, collecting meaningful data at the right time, ensuring data security, dealing with compliance issues, and handling data purging and storage problems. Common problems encountered during analysis include handling duplicate and missing values, collecting meaningful data at the right time, ensuring data security, dealing with compliance issues, and handling data purging and storage problems.', 'The chapter discusses the differences between data mining and data profiling, emphasizing the importance of data integrity and logic in both processes. The chapter discusses the differences between data mining and data profiling, emphasizing the importance of data integrity and logic in both processes.', 'Technical tools commonly used for analysis and presentation purposes include SQL Server, MySQL, Excel, SPSS, Tableau, and Python. Technical tools commonly used for analysis and presentation purposes include SQL Server, MySQL, Excel, SPSS, Tableau, and Python.']}, {'end': 585.462, 'start': 277.316, 'title': 'Data analysis best practices', 'summary': 'Covers best practices for data cleaning, handling missing values, understanding normal distribution, time series analysis, and differences between joining and blending in tableau.', 'duration': 308.146, 'highlights': ['Best Practices for Data Cleaning Focus on identifying and removing duplicates, maintaining data accuracy, and standardizing data at the point of entry to ensure an effective data analysis process. 80% of most data analysis is in cleaning the data.', 'Handling Missing Values Methods include listwise deletion, average imputation, regression substitution, and multiple imputation to fill in missing values, providing practical solutions for managing incomplete data sets.', 'Understanding Normal Distribution Explains normal distribution as a symmetric continuous probability distribution, emphasizing the properties such as the bell curve appearance, equal mean, median, and mode, and the percentage of data within standard deviations from the mean.', 'Time Series Analysis Defines time series analysis as a statistical method for analyzing ordered sequences of values at equally spaced time intervals, using COVID-19 cases as an example and highlighting its time-sensitive nature.', 'Differences Between Joining and Blending in Tableau Explains data joining and data blending in Tableau, highlighting the requirements and differences between the two methods for combining data from the same or different sources.', 'Overfitting vs Underfitting Discusses the risks of overfitting in data analytics, where a model learns noise and random fluctuations too well, impacting performance, and contrasts it with underfitting, providing insight into the dangers of both phenomena.']}], 'duration': 577.02, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI8442.jpg', 'highlights': ['Data wrangling involves cleaning, structuring, and enriching raw data for better decision making, with 80% of data analytics usually involving this step.', 'Best Practices for Data Cleaning focus on identifying and removing duplicates, maintaining data accuracy, and standardizing data at the point of entry to ensure an effective data analysis process. 80% of most data analysis is in cleaning the data.', 'Understanding the problem and interpreting the results are the most important steps in an analytics project.', 'Common problems encountered during analysis include handling duplicate and missing values, collecting meaningful data at the right time, ensuring data security, dealing with compliance issues, and handling data purging and storage problems.', 'The chapter discusses the differences between data mining and data profiling, emphasizing the importance of data integrity and logic in both processes.', 'Technical tools commonly used for analysis and presentation purposes include SQL Server, MySQL, Excel, SPSS, Tableau, and Python.', 'Handling Missing Values methods include listwise deletion, average imputation, regression substitution, and multiple imputation to fill in missing values, providing practical solutions for managing incomplete data sets.', 'Understanding Normal Distribution explains normal distribution as a symmetric continuous probability distribution, emphasizing the properties such as the bell curve appearance, equal mean, median, and mode, and the percentage of data within standard deviations from the mean.', 'Differences Between Joining and Blending in Tableau explains data joining and data blending in Tableau, highlighting the requirements and differences between the two methods for combining data from the same or different sources.', 'Overfitting vs Underfitting discusses the risks of overfitting in data analytics, where a model learns noise and random fluctuations too well, impacting performance, and contrasts it with underfitting, providing insight into the dangers of both phenomena.', 'Time Series Analysis defines time series analysis as a statistical method for analyzing ordered sequences of values at equally spaced time intervals, using COVID-19 cases as an example and highlighting its time-sensitive nature.']}, {'end': 969.161, 'segs': [{'end': 617.091, 'src': 'embed', 'start': 586.383, 'weight': 4, 'content': [{'end': 590.167, 'text': 'The model neither trains the data well nor can generalize to new data.', 'start': 586.383, 'duration': 3.784}, {'end': 593.471, 'text': 'Performs poorly both on train and the test set.', 'start': 590.447, 'duration': 3.024}, {'end': 600.298, 'text': 'Happens when there is less data to build and an accurate model and also when we try to build a linear model with a non-linear data.', 'start': 594.171, 'duration': 6.127}, {'end': 609.509, 'text': 'In Microsoft Excel, a numeric value can be treated as a text value if it proceeds with an apostrophe.', 'start': 601.946, 'duration': 7.563}, {'end': 611.409, 'text': 'Definitely not an exclamation.', 'start': 610.029, 'duration': 1.38}, {'end': 617.091, 'text': "If you're used to programming in Python, you'll look for that hash code and not an amber sign.", 'start': 611.429, 'duration': 5.662}], 'summary': 'Model performs poorly on both train and test sets, due to inadequate data and mismatched model type.', 'duration': 30.708, 'max_score': 586.383, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI586383.jpg'}, {'end': 733.254, 'src': 'heatmap', 'start': 630.208, 'weight': 1, 'content': [{'end': 644.934, 'text': 'What is the difference between count count A, count blank and count if in Excel? We can see here when we run in just count D1 through D23,', 'start': 630.208, 'duration': 14.726}, {'end': 646.275, 'text': 'we get 19..', 'start': 644.934, 'duration': 1.341}, {'end': 650.097, 'text': "And you'll notice that there is 19 numbers coming down here.", 'start': 646.275, 'duration': 3.822}, {'end': 654.599, 'text': "And so it doesn't count the cost of each, which is the top bracket.", 'start': 650.797, 'duration': 3.802}, {'end': 657.88, 'text': "It doesn't count the blank spaces either with the straight count.", 'start': 654.659, 'duration': 3.221}, {'end': 662.248, 'text': "When you do a count A, you'll get the answer is 20.", 'start': 658.746, 'duration': 3.502}, {'end': 667.132, 'text': 'So now when you do count A, it counts all of them, even the title cost of each.', 'start': 662.248, 'duration': 4.884}, {'end': 671.574, 'text': "When you do count blank, we'll get three.", 'start': 669.253, 'duration': 2.321}, {'end': 673.135, 'text': "Why? There's three blank fills.", 'start': 671.694, 'duration': 1.441}, {'end': 676.838, 'text': 'And finally, the count if.', 'start': 675.337, 'duration': 1.501}, {'end': 683.802, 'text': "If we do count if of E1 to E23 is greater than 10, there's 11 values in there.", 'start': 677.058, 'duration': 6.744}, {'end': 687.505, 'text': "Basic counting of whatever's in your column, pretty solid on the table there.", 'start': 684.003, 'duration': 3.502}, {'end': 692.164, 'text': 'Explain how VLOOKUP works in Excel.', 'start': 689.102, 'duration': 3.062}, {'end': 697.589, 'text': 'VLOOKUP is used when you need to find things in a table or a range by row.', 'start': 693.385, 'duration': 4.204}, {'end': 701.472, 'text': 'The syntax has four different parts to it.', 'start': 698.83, 'duration': 2.642}, {'end': 703.693, 'text': 'We have our lookup value.', 'start': 702.392, 'duration': 1.301}, {'end': 705.034, 'text': "That's a value you want to look up.", 'start': 703.753, 'duration': 1.281}, {'end': 706.556, 'text': 'We have our table array.', 'start': 705.355, 'duration': 1.201}, {'end': 710.579, 'text': 'The range where the lookup value is located.', 'start': 708.057, 'duration': 2.522}, {'end': 717.793, 'text': 'column index number, the column number and range that contains the return value, and the range lookup.', 'start': 711.749, 'duration': 6.044}, {'end': 723.657, 'text': 'Specify true if you want an approximate match or false if you want an exact match of the return value.', 'start': 718.033, 'duration': 5.624}, {'end': 733.254, 'text': 'So here we see VLOOKUP F3, A2 to C8, 2, 0 for Prince.', 'start': 725.925, 'duration': 7.329}], 'summary': 'Excel functions explained: count, counta, countblank, countif, and vlookup syntax and examples.', 'duration': 61.56, 'max_score': 630.208, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI630208.jpg'}, {'end': 830.346, 'src': 'embed', 'start': 805.127, 'weight': 0, 'content': [{'end': 814.193, 'text': 'How do you subset or filter data in SQL? To subset or filter data in SQL, we use WHERE and HAVING clause.', 'start': 805.127, 'duration': 9.066}, {'end': 820.283, 'text': 'You can see, we have a nice table on the left, where we have the title, the director, the year, the duration.', 'start': 815.04, 'duration': 5.243}, {'end': 825.025, 'text': 'We want to filter the table for movies that were directed by Brad Bird.', 'start': 821.003, 'duration': 4.022}, {'end': 828.247, 'text': 'Why? Just because we want to know what Brad Bird did.', 'start': 825.926, 'duration': 2.321}, {'end': 830.346, 'text': "So we're going to do select star.", 'start': 829.005, 'duration': 1.341}], 'summary': 'Filter data in sql using where and having clause to find movies directed by brad bird.', 'duration': 25.219, 'max_score': 805.127, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI805127.jpg'}, {'end': 886.939, 'src': 'embed', 'start': 854.865, 'weight': 2, 'content': [{'end': 858.788, 'text': "So we're going to take a closer look at the different ways we can filter it here.", 'start': 854.865, 'duration': 3.923}, {'end': 865.728, 'text': 'Filter the table for directors whose movies have an average duration greater than 115 minutes.', 'start': 859.363, 'duration': 6.365}, {'end': 872.213, 'text': "So there's a lot of really cool things into this SQL query, and these SQL queries can get pretty crazy.", 'start': 866.628, 'duration': 5.585}, {'end': 876.816, 'text': 'Select director sum duration as total duration.', 'start': 872.913, 'duration': 3.903}, {'end': 886.939, 'text': 'average duration as average duration from movies group by director having average duration greater than 115..', 'start': 876.816, 'duration': 10.123}], 'summary': 'Analyzing sql queries to filter directors with movies averaging over 115 minutes.', 'duration': 32.074, 'max_score': 854.865, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI854865.jpg'}], 'start': 586.383, 'title': 'Excel functions & vlookup and filtering data in sql', 'summary': "Covers challenges of model training, excel's treatment of numeric values, count functions, vlookup in excel, and transitioning to sql. it also explains how to subset or filter data in sql using where and having clauses, including specific examples, emphasizing the importance of understanding basic sql.", 'chapters': [{'end': 804.947, 'start': 586.383, 'title': 'Excel functions & vlookup', 'summary': "Covers the challenges of model training and generalization, excel's treatment of numeric values as text, differences between count functions in excel, and the workings of vlookup in excel, with a brief mention of transitioning to sql.", 'duration': 218.564, 'highlights': ["VLOOKUP syntax breakdown The VLOOKUP function in Excel is explained, involving the lookup value, table array, column index number, and range lookup, with an example of using VLOOKUP to retrieve data such as 'Prince' and 'Angela' from specified columns.", 'Count functions in Excel The differences between count, count A, count blank, and count if functions in Excel are highlighted, demonstrating their distinct behaviors and providing quantifiable data such as the counts for each function.', 'Challenges of model training and generalization The challenges of model training and generalization are discussed, emphasizing poor performance on both train and test sets due to insufficient data for accurate model building and attempting to fit linear models to non-linear data.', "Excel's treatment of numeric values as text The behavior of treating numeric values as text in Excel when preceded by an apostrophe is described, illustrated with an example of entering '10 into a cell with an apostrophe resulting in its interpretation as text instead of a number."]}, {'end': 969.161, 'start': 805.127, 'title': 'Filtering and subsetting data in sql', 'summary': 'Explains how to subset or filter data in sql using where and having clauses, including filtering movies directed by brad bird and directors with an average movie duration greater than 115 minutes, emphasizing the importance of understanding basic sql and distinguishing between where and having clauses.', 'duration': 164.034, 'highlights': ['The chapter explains how to subset or filter data in SQL using WHERE and HAVING clauses. Explains the primary focus of the chapter.', 'Filtering movies directed by Brad Bird by using the WHERE clause to return all titles directed by Brad Bird. Demonstrates the use of the WHERE clause to filter movies directed by Brad Bird.', 'Filtering directors with an average movie duration greater than 115 minutes using the HAVING clause and aggregate functions. Illustrates the use of the HAVING clause and aggregate functions to filter directors with an average movie duration greater than 115 minutes.', 'Emphasizes the importance of understanding basic SQL and its relevance in various domains including Hadoop. Stresses the significance of comprehending basic SQL and its applicability in various domains.', 'Explains the difference between WHERE and HAVING clauses in SQL, detailing their respective functionalities and limitations. Provides an explanation of the difference between WHERE and HAVING clauses in SQL.']}], 'duration': 382.778, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI586383.jpg', 'highlights': ['The chapter explains how to subset or filter data in SQL using WHERE and HAVING clauses. Explains the primary focus of the chapter.', "VLOOKUP syntax breakdown The VLOOKUP function in Excel is explained, involving the lookup value, table array, column index number, and range lookup, with an example of using VLOOKUP to retrieve data such as 'Prince' and 'Angela' from specified columns.", 'Filtering directors with an average movie duration greater than 115 minutes using the HAVING clause and aggregate functions. Illustrates the use of the HAVING clause and aggregate functions to filter directors with an average movie duration greater than 115 minutes.', 'Count functions in Excel The differences between count, count A, count blank, and count if functions in Excel are highlighted, demonstrating their distinct behaviors and providing quantifiable data such as the counts for each function.', 'Challenges of model training and generalization The challenges of model training and generalization are discussed, emphasizing poor performance on both train and test sets due to insufficient data for accurate model building and attempting to fit linear models to non-linear data.']}, {'end': 1634.516, 'segs': [{'end': 1114.391, 'src': 'embed', 'start': 1087.484, 'weight': 7, 'content': [{'end': 1094.609, 'text': 'To create a data frame in Python, you need to import the pandas library and use the readcsv function to load the csv file.', 'start': 1087.484, 'duration': 7.125}, {'end': 1107.489, 'text': 'here you can see we have import pandas as pd employees or the data frame employees equals pd.readcsv, and then you have your path to that csv file.', 'start': 1095.666, 'duration': 11.823}, {'end': 1114.391, 'text': "there's a number of settings in the read csv where you can tell it how many rows are the top index.", 'start': 1107.489, 'duration': 6.902}], 'summary': 'To create a data frame in python using pandas, import the library and use the readcsv function to load the csv file.', 'duration': 26.907, 'max_score': 1087.484, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI1087484.jpg'}, {'end': 1182.179, 'src': 'embed', 'start': 1153.266, 'weight': 1, 'content': [{'end': 1157.469, 'text': "But if you're doing multiple columns, you've got to have those in a second set of brackets.", 'start': 1153.266, 'duration': 4.203}, {'end': 1160.731, 'text': "It's got to be a reference with a list within the reference.", 'start': 1157.549, 'duration': 3.182}, {'end': 1171.991, 'text': 'What is the criteria to say whether a developed data model is good or not? A good model should be intuitive, insightful, and self-explanatory.', 'start': 1162.612, 'duration': 9.379}, {'end': 1175.254, 'text': 'Follow the old saying, KISS, keep it simple.', 'start': 1172.672, 'duration': 2.582}, {'end': 1182.179, 'text': 'The model developed should be able to easily be consumed by the clients for actionable and profitable results.', 'start': 1176.015, 'duration': 6.164}], 'summary': 'A good data model should be intuitive, insightful, and self-explanatory, following the kiss principle for easy client consumption.', 'duration': 28.913, 'max_score': 1153.266, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI1153266.jpg'}, {'end': 1288.44, 'src': 'embed', 'start': 1217.597, 'weight': 0, 'content': [{'end': 1221.318, 'text': 'Exploratory Data Analysis helps to understand the data better.', 'start': 1217.597, 'duration': 3.721}, {'end': 1227.4, 'text': "It helps you obtain confidence in your data to a point where you're ready to engage a machine learning algorithm.", 'start': 1221.879, 'duration': 5.521}, {'end': 1232.942, 'text': 'It allows you to refine your selection of feature variables that will be used later for model building.', 'start': 1227.961, 'duration': 4.981}, {'end': 1236.804, 'text': 'You can discover hidden trends and insights from the data.', 'start': 1233.563, 'duration': 3.241}, {'end': 1244.793, 'text': 'How do you treat outliers in a dataset? An outlier is a data point that is distant from other similar points.', 'start': 1237.864, 'duration': 6.929}, {'end': 1249.517, 'text': 'They may be due to variability in the measurement or may indicate experimental errors.', 'start': 1245.173, 'duration': 4.344}, {'end': 1253.099, 'text': 'One, you can drop the outlier records.', 'start': 1251.038, 'duration': 2.061}, {'end': 1254.2, 'text': 'Pretty straightforward.', 'start': 1253.36, 'duration': 0.84}, {'end': 1259.244, 'text': "You can cap your outlier's data so it doesn't go past a certain value.", 'start': 1254.58, 'duration': 4.664}, {'end': 1261.746, 'text': 'You can assign it a new value.', 'start': 1259.945, 'duration': 1.801}, {'end': 1267.951, 'text': 'You can also try a new transformation to see if those outliers come in if you transform it slightly differently.', 'start': 1262.306, 'duration': 5.645}, {'end': 1274.413, 'text': 'Explain descriptive, predictive, and prescriptive analytics.', 'start': 1268.911, 'duration': 5.502}, {'end': 1279.536, 'text': 'Descriptive provides insights into the past to answer what has happened.', 'start': 1275.134, 'duration': 4.402}, {'end': 1283.818, 'text': 'Uses data aggregation and data mining techniques.', 'start': 1280.356, 'duration': 3.462}, {'end': 1288.44, 'text': 'Examples, an ice cream company can analyze how much ice cream was sold,', 'start': 1284.198, 'duration': 4.242}], 'summary': 'Exploratory data analysis refines feature selection, detects outliers, and enables insights for decision-making.', 'duration': 70.843, 'max_score': 1217.597, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI1217597.jpg'}, {'end': 1397.743, 'src': 'embed', 'start': 1372.401, 'weight': 3, 'content': [{'end': 1379.865, 'text': 'How do these different things directly affect the end and can we create a better ending by changing some underlying criteria?', 'start': 1372.401, 'duration': 7.464}, {'end': 1385.155, 'text': 'What are the different types of sampling techniques used by data analysis?', 'start': 1381.152, 'duration': 4.003}, {'end': 1395.081, 'text': 'Sampling is a statistical method to select a subset of data from an entire data set population to estimate the characteristics of the whole population.', 'start': 1386.075, 'duration': 9.006}, {'end': 1397.743, 'text': 'One, we can do a simple random sampling.', 'start': 1395.901, 'duration': 1.842}], 'summary': 'Exploring impact of different factors on outcomes, focusing on sampling techniques for data analysis.', 'duration': 25.342, 'max_score': 1372.401, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI1372401.jpg'}, {'end': 1528.958, 'src': 'embed', 'start': 1500.78, 'weight': 5, 'content': [{'end': 1504.445, 'text': 'We have null hypothesis and alternative hypothesis.', 'start': 1500.78, 'duration': 3.665}, {'end': 1512.69, 'text': 'On the null hypothesis, It states that there is no relation between the predictor and the outcome variables in the population.', 'start': 1505.486, 'duration': 7.204}, {'end': 1515.111, 'text': 'It is denoted by H naught.', 'start': 1513.29, 'duration': 1.821}, {'end': 1521.254, 'text': 'Example, there is no association between patients, BMI, and diabetes.', 'start': 1515.911, 'duration': 5.343}, {'end': 1528.958, 'text': 'Alternative hypothesis, it states there is some relation between the predictor and outcome variables in the population.', 'start': 1522.154, 'duration': 6.804}], 'summary': 'Null hypothesis: no relation between variables. alternative hypothesis: there is a relation.', 'duration': 28.178, 'max_score': 1500.78, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI1500780.jpg'}, {'end': 1608.248, 'src': 'embed', 'start': 1582.518, 'weight': 4, 'content': [{'end': 1587.022, 'text': 'Example, analyzing sale of ice creams based on the temperature outside.', 'start': 1582.518, 'duration': 4.504}, {'end': 1597.825, 'text': 'Bivariate analysis can be explained using correlation coefficients, linear regression, logistic regression, scatter plots, and box plots.', 'start': 1587.782, 'duration': 10.043}, {'end': 1608.248, 'text': 'And multivariate analysis, it involves analysis of three or more variables to understand the relationship of each variable with the other variables.', 'start': 1599.366, 'duration': 8.882}], 'summary': 'Analyzing ice cream sales based on temperature using bivariate and multivariate analysis.', 'duration': 25.73, 'max_score': 1582.518, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI1582518.jpg'}], 'start': 969.946, 'title': 'Data analysis and modeling essentials', 'summary': 'Covers the essentials of data analysis and modeling, including criteria for a good data model, significance of exploratory data analysis, treatment of outliers, types of analytics, sampling techniques, hypothesis testing, and types of data analysis.', 'chapters': [{'end': 1152.886, 'start': 969.946, 'title': 'Numpy reshape and pandas dataframe creation', 'summary': 'Covers the correct syntax for reshaping in numpy using numpy.reshape, different ways to create a data frame in pandas, and how to create an employees data frame from a csv file in python.', 'duration': 182.94, 'highlights': ["The correct syntax for reshaping in numpy is using numpy.reshape, often preceded by 'import numpy as np', and then specifying the array and the new shape, resulting in a reshaped array with specified dimensions such as 2 rows with 5 values in each.", 'Pandas data frame can be created by initializing a list or from a dictionary, allowing the designation of columns and index. Additionally, the Python code to create an employees data frame from the emp.csv file is demonstrated by importing the pandas library and using the readcsv function to load the csv file, with options to specify settings such as top index rows, columns, and skip rows.', "To select the department and age columns from an employee's data frame, the Pandas library is imported, and the employee's data frame is created, and then the department and age columns are selected using the notation employees['department'] and employees['age']."]}, {'end': 1634.516, 'start': 1153.266, 'title': 'Data analysis and modeling essentials', 'summary': 'Covers the essentials of data analysis and modeling, including criteria for a good data model, significance of exploratory data analysis, treatment of outliers, types of analytics, sampling techniques, hypothesis testing, and types of data analysis.', 'duration': 481.25, 'highlights': ['Criteria for a Good Data Model A good data model should be intuitive, insightful, self-explanatory, easily consumed by clients, easily adaptable to changes, and scalable to new data, adhering to the KISS principle (Keep it Simple).', 'Significance of Exploratory Data Analysis Exploratory Data Analysis is crucial for understanding data, building confidence for machine learning, refining feature selection, and uncovering hidden trends and insights, aiding in making actionable and profitable decisions.', 'Treatment of Outliers Outliers in a dataset can be addressed by dropping them, capping their values, assigning them new values, or trying different transformations to mitigate their impact, ensuring data integrity and robust modeling.', 'Types of Analytics Descriptive analytics provides insights into past events, predictive analytics forecasts future outcomes, and prescriptive analytics advises on potential actions, with examples related to sales analysis and addressing the COVID-19 pandemic.', 'Sampling Techniques in Data Analysis Sampling techniques include simple random sampling, systematic sampling, cluster sampling, stratified sampling, and judgmental or purposive sampling, offering varied approaches to gathering representative data subsets for analysis.', 'Hypothesis Testing Hypothesis testing involves accepting or rejecting null and alternative hypotheses, exploring the presence or absence of relationships between predictor and outcome variables, providing a structured approach to statistical inference.', 'Types of Data Analysis Univariate analysis focuses on a single variable, bivariate analysis examines two variables for relationships and correlations, while multivariate analysis involves analyzing three or more variables to understand complex relationships and interdependencies.']}], 'duration': 664.57, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI969946.jpg', 'highlights': ['Significance of Exploratory Data Analysis Exploratory Data Analysis is crucial for understanding data, building confidence for machine learning, refining feature selection, and uncovering hidden trends and insights, aiding in making actionable and profitable decisions.', 'Criteria for a Good Data Model A good data model should be intuitive, insightful, self-explanatory, easily consumed by clients, easily adaptable to changes, and scalable to new data, adhering to the KISS principle (Keep it Simple).', 'Types of Analytics Descriptive analytics provides insights into past events, predictive analytics forecasts future outcomes, and prescriptive analytics advises on potential actions, with examples related to sales analysis and addressing the COVID-19 pandemic.', 'Sampling Techniques in Data Analysis Sampling techniques include simple random sampling, systematic sampling, cluster sampling, stratified sampling, and judgmental or purposive sampling, offering varied approaches to gathering representative data subsets for analysis.', 'Types of Data Analysis Univariate analysis focuses on a single variable, bivariate analysis examines two variables for relationships and correlations, while multivariate analysis involves analyzing three or more variables to understand complex relationships and interdependencies.', 'Hypothesis Testing Hypothesis testing involves accepting or rejecting null and alternative hypotheses, exploring the presence or absence of relationships between predictor and outcome variables, providing a structured approach to statistical inference.', 'Treatment of Outliers Outliers in a dataset can be addressed by dropping them, capping their values, assigning them new values, or trying different transformations to mitigate their impact, ensuring data integrity and robust modeling.', 'Pandas data frame can be created by initializing a list or from a dictionary, allowing the designation of columns and index. Additionally, the Python code to create an employees data frame from the emp.csv file is demonstrated by importing the pandas library and using the readcsv function to load the csv file, with options to specify settings such as top index rows, columns, and skip rows.']}, {'end': 2255.339, 'segs': [{'end': 1708.287, 'src': 'heatmap', 'start': 1660.559, 'weight': 0, 'content': [{'end': 1663.94, 'text': 'and the cost of each item they have sold is greater than 10..', 'start': 1660.559, 'duration': 3.381}, {'end': 1672.19, 'text': 'And you can see here on the left we have our actual table And then we want to go ahead and sum ifs.', 'start': 1663.94, 'duration': 8.25}, {'end': 1679.372, 'text': 'So we want the E2 through E20, B2 through B20 greater than 10.', 'start': 1672.63, 'duration': 6.742}, {'end': 1687.275, 'text': "And this, basically is just saying hey, we're going to take everything in the E column and we're going to sum it up,", 'start': 1679.372, 'duration': 7.903}, {'end': 1691.036, 'text': 'but only those objects where the D column is greater than 10..', 'start': 1687.275, 'duration': 3.761}, {'end': 1692.516, 'text': "That's what that means there.", 'start': 1691.036, 'duration': 1.48}, {'end': 1695.677, 'text': 'Is the below query correct?', 'start': 1694.177, 'duration': 1.5}, {'end': 1698.138, 'text': 'If not, how will you rectify it?', 'start': 1696.057, 'duration': 2.081}, {'end': 1708.287, 'text': 'select customer ID year, order date as order year from order where order year is greater than or equal to 2016,', 'start': 1699.184, 'duration': 9.103}], 'summary': 'Sum up the costs of items sold if their price is greater than 10. query for orders after 2016 is incorrect.', 'duration': 26.716, 'max_score': 1660.559, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI1660559.jpg'}, {'end': 1759.485, 'src': 'embed', 'start': 1734.609, 'weight': 2, 'content': [{'end': 1744.995, 'text': 'How are union, intersect, and except used in SQL? The union operator is used to combine the results of two or more select statements.', 'start': 1734.609, 'duration': 10.386}, {'end': 1752.54, 'text': "And you can see here we have select star from region 1 and we're going to make a union with select star from region 2.", 'start': 1746.056, 'duration': 6.484}, {'end': 1759.485, 'text': 'And it basically takes both these SQL tables and combines them to form a full new table on there.', 'start': 1752.54, 'duration': 6.945}], 'summary': 'Sql union operator combines results of select statements from different tables to form a new table.', 'duration': 24.876, 'max_score': 1734.609, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI1734609.jpg'}, {'end': 1839.323, 'src': 'embed', 'start': 1810.055, 'weight': 3, 'content': [{'end': 1810.916, 'text': "They're always fun.", 'start': 1810.055, 'duration': 0.861}, {'end': 1819.687, 'text': "And the first thing we want to do is we're going to go ahead and if you look at the script on the left, we really want the fourth one down.", 'start': 1810.936, 'duration': 8.751}, {'end': 1822.87, 'text': "So we're going to select the top four from product price.", 'start': 1819.787, 'duration': 3.083}, {'end': 1827.075, 'text': "But we're going to order it by market price descending.", 'start': 1823.451, 'duration': 3.624}, {'end': 1829.799, 'text': 'SP order by market price ascending.', 'start': 1827.616, 'duration': 2.183}, {'end': 1839.323, 'text': "So what we do is we take the top four of the market price ascending, and that's going to give us the four greatest values.", 'start': 1830.714, 'duration': 8.609}], 'summary': 'Select top 4 product prices, order by market price descending.', 'duration': 29.268, 'max_score': 1810.055, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI1810055.jpg'}, {'end': 2037.662, 'src': 'embed', 'start': 2008.848, 'weight': 1, 'content': [{'end': 2014.109, 'text': 'Under technology category, copiers made the highest profit, though it was the least amount of sales.', 'start': 2008.848, 'duration': 5.261}, {'end': 2024.453, 'text': "Let's work to create a dual axes chart in Tableau to present sales and profits across different years using sample Superstore data set.", 'start': 2015.75, 'duration': 8.703}, {'end': 2029.134, 'text': 'Load the orders sheet from the sample Superstore data set.', 'start': 2025.353, 'duration': 3.781}, {'end': 2037.662, 'text': 'Drag the order data field from the dimensions onto columns and convert it into continuous month.', 'start': 2030.68, 'duration': 6.982}], 'summary': 'Copiers had highest profit in technology category with least sales.', 'duration': 28.814, 'max_score': 2008.848, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI2008848.jpg'}], 'start': 1635.957, 'title': 'Data analysis and visualization tools', 'summary': 'Covers excel functions today and now, sumifs function for quantity sold and cost, sql operators union, intersect, except, and writing sql queries. it also discusses using tableau to create visualizations for sales and profits analysis, including creating bar charts and dual axes charts, and presents key insights on highest sales and profits by category and state, using the sample superstore dataset.', 'chapters': [{'end': 1921.602, 'start': 1635.957, 'title': 'Excel, sql, tableau: functions and queries', 'summary': 'Covers excel functions today and now, sumifs function for quantity sold and cost, sql operators union, intersect, except, and writing sql queries to find highest market price and average market price in specific currencies.', 'duration': 285.645, 'highlights': ['Using the SUMIFS function in Excel, find the total quantity sold by sales representatives whose names start with A and the cost of each item they have sold is greater than 10. The SUMIFS function in Excel can be used to find the total quantity sold by sales representatives whose names start with A and the cost of each item they have sold is greater than 10.', 'The union operator is used to combine the results of two or more select statements in SQL. The union operator in SQL is used to combine the results of two or more select statements, effectively forming a full new table.', 'Writing an SQL query to find the record with the fourth highest market price involves using the SELECT TOP and ORDER BY clauses. To find the record with the fourth highest market price in SQL, the SELECT TOP and ORDER BY clauses are used to select and order the values accordingly.', 'Find the total and average market price for each currency in SQL, where the average market price is greater than 100 and currency is INR or AUD. In SQL, finding the total and average market price for each currency involves using the GROUP BY and HAVING clauses to filter and aggregate the data, ensuring the average market price is greater than 100 and the currency is either INR or AUD.']}, {'end': 2255.339, 'start': 1921.602, 'title': 'Tableau data analysis and visualization', 'summary': 'Discusses using tableau to create visualizations for sales and profits analysis, including creating bar charts and dual axes charts, and presents key insights on highest sales and profits by category and state, using the sample superstore dataset.', 'duration': 333.737, 'highlights': ['Visualizing sales and profits by category using Tableau The chapter demonstrates creating a horizontal bar chart in Tableau to analyze sales profits and quantity sold across different subcategories, revealing insights such as the highest profit in the furniture category from chairs and the least profit from tables.', 'Creating dual axes charts for sales and profits analysis Instructions are provided to create a dual axes chart in Tableau to present sales and profits across different years, utilizing the sample superstore dataset, with insights on highest sales and profits by state.', 'Utilizing numpy for array manipulation in Python The transcript briefly covers the use of numpy in Python for array manipulation, including examples of 2D indexing and logical statements to extract specific values from an array.']}], 'duration': 619.382, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI1635957.jpg', 'highlights': ['Using the SUMIFS function in Excel to find total quantity sold and cost by sales reps', 'Creating dual axes charts in Tableau for sales and profits analysis', 'Using the union operator in SQL to combine results of select statements', 'Writing SQL query to find record with the fourth highest market price', 'Visualizing sales and profits by category using Tableau']}, {'end': 2581.897, 'segs': [{'end': 2278.605, 'src': 'embed', 'start': 2256.98, 'weight': 3, 'content': [{'end': 2266.066, 'text': 'How can you add a column to a pandas data frame? Suppose there is an imp data frame that has information about few employees.', 'start': 2256.98, 'duration': 9.086}, {'end': 2269.609, 'text': "Let's add address column to that data frame.", 'start': 2266.547, 'duration': 3.062}, {'end': 2272.25, 'text': 'And you can see in the left, we have our basic data frame.', 'start': 2270.109, 'duration': 2.141}, {'end': 2274.312, 'text': 'You should know your data frames very well.', 'start': 2272.27, 'duration': 2.042}, {'end': 2276.393, 'text': 'Basically, it looks like an Excel spreadsheet.', 'start': 2274.892, 'duration': 1.501}, {'end': 2278.605, 'text': "As you come over here, it's really simple.", 'start': 2276.983, 'duration': 1.622}], 'summary': 'Add address column to pandas data frame. understand data frames well.', 'duration': 21.625, 'max_score': 2256.98, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI2256980.jpg'}, {'end': 2378.159, 'src': 'embed', 'start': 2330.56, 'weight': 0, 'content': [{'end': 2338.122, 'text': 'And finally, right click on sum of cell total and expand show values as to select percentage of grand total.', 'start': 2330.56, 'duration': 7.562}, {'end': 2342.108, 'text': 'Real important just to understand what a pivot table is.', 'start': 2339.706, 'duration': 2.402}, {'end': 2346.29, 'text': "We're just pivoting it from rows and columns and switching this direction on there.", 'start': 2342.148, 'duration': 4.142}, {'end': 2352.934, 'text': 'And finally, we have our final pivot table, and you can see the values, roles, and sum of total sale.', 'start': 2347.611, 'duration': 5.323}, {'end': 2356.696, 'text': "So we're going to go ahead and take a product table.", 'start': 2354.355, 'duration': 2.341}, {'end': 2360.158, 'text': "This is off of an SQL, so we're going to do some SQL here.", 'start': 2356.916, 'duration': 3.242}, {'end': 2365.211, 'text': "And we're going to use the product and sales order detail table.", 'start': 2361.328, 'duration': 3.883}, {'end': 2370.694, 'text': 'Find the products that have total units sold greater than 1.5 million.', 'start': 2365.391, 'duration': 5.303}, {'end': 2373.436, 'text': "And here's our sales order detail table.", 'start': 2371.214, 'duration': 2.222}, {'end': 2378.159, 'text': 'So we have a product table and a sales order detail table, two separate tables in the database.', 'start': 2373.556, 'duration': 4.603}], 'summary': 'Pivoted table with sales data from sql, finding products with units sold > 1.5m.', 'duration': 47.599, 'max_score': 2330.56, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI2330560.jpg'}, {'end': 2454.109, 'src': 'embed', 'start': 2413.777, 'weight': 2, 'content': [{'end': 2420.145, 'text': 'And again, these SQL queries, they start looking really crazy until you just break them apart and do them step by step.', 'start': 2413.777, 'duration': 6.368}, {'end': 2425.906, 'text': "And what we're looking for is the inner join, and how did you do the group by this?", 'start': 2421.044, 'duration': 4.862}, {'end': 2428.587, 'text': 'really wanted to know how do you do this inner join.', 'start': 2425.906, 'duration': 2.681}, {'end': 2430.548, 'text': 'this comes up so much in sql.', 'start': 2428.587, 'duration': 1.961}, {'end': 2437.671, 'text': 'How do you pull in the ID from one chart and the information from another chart and the sum totals on that chart.', 'start': 2431.428, 'duration': 6.243}, {'end': 2444.254, 'text': "How do you write a stored procedure in sql let's create a storage procedure to find the sum.", 'start': 2439.152, 'duration': 5.102}, {'end': 2447.501, 'text': 'The squares are the first n natural numbers.', 'start': 2444.858, 'duration': 2.643}, {'end': 2454.109, 'text': 'So here we have our formula, n times n plus 1 times 2n plus 1 over 6.', 'start': 2447.942, 'duration': 6.167}], 'summary': 'The transcript discusses sql queries, including inner join, group by, and stored procedure, as well as a formula for summing natural numbers.', 'duration': 40.332, 'max_score': 2413.777, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI2413777.jpg'}, {'end': 2537.419, 'src': 'embed', 'start': 2507.532, 'weight': 5, 'content': [{'end': 2515.336, 'text': 'Write a store procedure to find the total even number between two user-given numbers.', 'start': 2507.532, 'duration': 7.804}, {'end': 2518.177, 'text': 'couple things to note here.', 'start': 2516.516, 'duration': 1.661}, {'end': 2519.558, 'text': 'first, we go ahead and create our procedure.', 'start': 2518.177, 'duration': 1.381}, {'end': 2524.641, 'text': 'you have your two different variables the n1, n2 and we go ahead and begin.', 'start': 2519.558, 'duration': 5.083}, {'end': 2527.403, 'text': "we're going to declare our variable count as an integer.", 'start': 2524.641, 'duration': 2.762}, {'end': 2537.419, 'text': "we're going to set count equal to zero and then we have while n is less than n2, we're going to begin and if n1 Remainder 2 equals 0,", 'start': 2527.403, 'duration': 10.016}], 'summary': 'Create a stored procedure to find total even numbers between user-given inputs.', 'duration': 29.887, 'max_score': 2507.532, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI2507532.jpg'}], 'start': 2256.98, 'title': 'Data frame operations and sql stored procedures', 'summary': 'Covers adding columns to a pandas data frame, creating a pivot table, and executing a sql query to find products with total units sold greater than 1.5 million, along with creating stored procedures in sql to find the sum of squares for natural numbers and the total even numbers between user-given numbers.', 'chapters': [{'end': 2430.548, 'start': 2256.98, 'title': 'Pandas data frame and sql query operations', 'summary': 'Covers adding a column to a pandas data frame and creating a pivot table in tableau, and then delves into constructing a sql query to find products with total units sold greater than 1.5 million.', 'duration': 173.568, 'highlights': ['The process of adding a column to a pandas data frame is explained, involving assigning values to the new column and understanding the structure of data frames, resembling Excel spreadsheets.', 'The demonstration of creating a pivot table in Tableau is detailed, including selecting table ranges, placing the pivot table, and displaying cells as a percentage of the grand total to analyze data.', 'Constructing a SQL query to find products with total units sold greater than 1.5 million is depicted, involving the selection of specific columns, inner join, group by, and the condition for sum of unit price.', 'The importance of understanding the inner join and group by in SQL queries is emphasized, with a recommendation to break down complex queries into manageable steps for better comprehension.']}, {'end': 2581.897, 'start': 2431.428, 'title': 'Sql stored procedure examples', 'summary': 'Covers creating stored procedures in sql to find the sum of squares for natural numbers and the total even numbers between user-given numbers, demonstrating the execution and results of each procedure.', 'duration': 150.469, 'highlights': ['A stored procedure is created to find the sum of squares for the first four natural numbers, yielding a sum of 30.', 'A stored procedure is written to find the total even numbers between 30 and 45, resulting in 8 even numbers.']}], 'duration': 324.917, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI2256980.jpg', 'highlights': ['Creating a pivot table in Tableau, including selecting table ranges and displaying cells as a percentage of the grand total', 'Constructing a SQL query to find products with total units sold greater than 1.5 million, involving inner join and group by', 'Importance of understanding inner join and group by in SQL queries, with a recommendation to break down complex queries', 'Demonstration of adding a column to a pandas data frame, involving assigning values to the new column and understanding the structure of data frames', 'Creating a stored procedure to find the sum of squares for the first four natural numbers, yielding a sum of 30', 'Writing a stored procedure to find the total even numbers between 30 and 45, resulting in 8 even numbers']}, {'end': 2987.25, 'segs': [{'end': 2610.934, 'src': 'embed', 'start': 2581.897, 'weight': 4, 'content': [{'end': 2586.159, 'text': 'What is the difference between tree maps and heat maps in Tableau?', 'start': 2581.897, 'duration': 4.262}, {'end': 2591.683, 'text': "Now, if you've worked in Python and other programmings, you should automatically know what a heat map is.", 'start': 2587.12, 'duration': 4.563}, {'end': 2596.926, 'text': 'But a tree map are used to display data in nested rectangles.', 'start': 2592.703, 'duration': 4.223}, {'end': 2604.19, 'text': 'You use dimensions to define the structure of the tree map and measure to define the size or color of individual rectangles.', 'start': 2597.506, 'duration': 6.684}, {'end': 2610.934, 'text': 'Tree maps are a relatively simple data visualization that can provide insight in a visually attractive format.', 'start': 2605.01, 'duration': 5.924}], 'summary': 'Tree maps display nested rectangles, providing data insight visually in tableau.', 'duration': 29.037, 'max_score': 2581.897, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI2581897.jpg'}, {'end': 2647.476, 'src': 'embed', 'start': 2622.02, 'weight': 3, 'content': [{'end': 2630.306, 'text': 'A heat map helps to visualize measures against dimensions with the help of colors and size to compare one or more dimensions and up to two measures.', 'start': 2622.02, 'duration': 8.286}, {'end': 2635.731, 'text': 'The layout is similar to a text table with variations in values encoded as colors.', 'start': 2631.067, 'duration': 4.664}, {'end': 2639.554, 'text': 'In heat map, you can quickly see a wide array of information.', 'start': 2636.351, 'duration': 3.203}, {'end': 2647.476, 'text': 'And in this one, you can see they use the colors to denote one thing and the size of the little square to denote something else.', 'start': 2640.43, 'duration': 7.046}], 'summary': 'Heat maps visualize data with colors and size, aiding quick information absorption.', 'duration': 25.456, 'max_score': 2622.02, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI2622020.jpg'}, {'end': 2694.754, 'src': 'embed', 'start': 2665.748, 'weight': 0, 'content': [{'end': 2670.229, 'text': 'So you start by dragging the customer name field onto rows and profit on columns.', 'start': 2665.748, 'duration': 4.481}, {'end': 2674.31, 'text': 'Right click on the customer name column to create a set.', 'start': 2671.089, 'duration': 3.221}, {'end': 2680.971, 'text': 'Give a name to the set and select top tab to choose top five customers by some profit.', 'start': 2675.41, 'duration': 5.561}, {'end': 2686.132, 'text': 'Similarly, create a set for the bottom five customers by some profit.', 'start': 2681.931, 'duration': 4.201}, {'end': 2691.253, 'text': 'Select both the sets, right click to create a combined set.', 'start': 2687.572, 'duration': 3.681}, {'end': 2694.754, 'text': 'Give a name to the set and choose all members in both sets.', 'start': 2691.793, 'duration': 2.961}], 'summary': 'Using tableau, create sets for top and bottom five customers by profit, then combine sets to choose all members.', 'duration': 29.006, 'max_score': 2665.748, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI2665748.jpg'}, {'end': 2770.966, 'src': 'embed', 'start': 2717.594, 'weight': 1, 'content': [{'end': 2722.718, 'text': 'To generate random numbers using numpy, we use the random integer function.', 'start': 2717.594, 'duration': 5.124}, {'end': 2734.534, 'text': 'You can see here we did the import numpy as np, random arrangement equals np.random.randomInteger 1 through 15 of 4.', 'start': 2722.738, 'duration': 11.796}, {'end': 2739.195, 'text': 'From the below data frame.', 'start': 2734.534, 'duration': 4.661}, {'end': 2749.418, 'text': 'how will you find the unique values for each column and subset the data for age less than 35 and height greater than 6?', 'start': 2739.195, 'duration': 10.223}, {'end': 2756.14, 'text': 'To find the unique values and the number of unique elements, use the unique and the inUnique function.', 'start': 2749.418, 'duration': 6.722}, {'end': 2759.422, 'text': 'You see, here we just did df heights.', 'start': 2757.461, 'duration': 1.961}, {'end': 2762.923, 'text': "we're selecting just the height column and we want to look for the unique.", 'start': 2759.422, 'duration': 3.501}, {'end': 2770.966, 'text': 'that returns an array where, in unique, if we do that on the height or the age, return just the number of unique values.', 'start': 2762.923, 'duration': 8.043}], 'summary': 'Using numpy to generate random numbers and subset data based on age and height', 'duration': 53.372, 'max_score': 2717.594, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI2717594.jpg'}], 'start': 2581.897, 'title': 'Comparing tree maps and heat maps', 'summary': 'Compares tree maps and heat maps in tableau, highlighting their differences in visualization techniques and also demonstrates how to display top and bottom customers based on profit and generate random integers using numpy. additionally, it covers data visualization and analysis in python, including plotting a signed graph, finding the company with the highest average sales, and deriving summary statistics for the sales column using pandas.', 'chapters': [{'end': 2797.679, 'start': 2581.897, 'title': 'Tree maps vs heat maps in tableau', 'summary': 'Compares tree maps and heat maps in tableau, highlighting that tree maps use dimensions to define the structure and measure to define the size or color of rectangles, while heat maps visualize measures against dimensions using colors and size to compare data. it also demonstrates how to display top and bottom customers based on profit and how to generate random integers using numpy.', 'duration': 215.782, 'highlights': ['Tree maps use dimensions to define the structure and measure to define the size or color of individual rectangles. Tree maps provide a visually attractive format for displaying data and can offer insight into the data. Each block in the tree map contains information and is based on the defined dimensions and measures.', 'Heat maps visualize measures against dimensions using colors and size to compare one or more dimensions and up to two measures. Heat maps help visualize a wide array of information by encoding variations in values as colors. The layout is similar to a text table, and it allows quick comparison of data using colors and size.', 'Demonstration of displaying the top and bottom customers based on profit in Tableau. The chapter provides a step-by-step guide on how to display the top and bottom customers based on their profit using the sample superstore dataset in Tableau. It includes creating sets, selecting top or bottom customers, and applying filters to achieve desired results.', 'Guidance on generating random integers between 1 and 15 using NumPy. The chapter demonstrates how to use NumPy to generate four random integers between 1 and 15. It involves importing NumPy, using the random integer function, and specifying the range and number of random integers to generate.', 'Method for finding unique values for each column and subsetting data based on specified conditions using pandas. The chapter explains how to use pandas to find unique values for each column and subset the data based on specific conditions, such as age less than 35 and height greater than 6. It involves utilizing the unique function to find unique values and creating a new subset of the data based on the specified criteria.']}, {'end': 2987.25, 'start': 2797.679, 'title': 'Data visualization and analysis in python', 'summary': 'Covers plotting a signed graph using numpy and matplotlibrary in python, followed by a demonstration of using pandas to find the company with the highest average sales and deriving summary statistics for the sales column.', 'duration': 189.571, 'highlights': ["The chapter covers plotting a signed graph using numpy and matplotlibrary in Python, demonstrating how to generate x and y values and visualize the graph using matplotlib.pyplot, with a tip on using the 'matplotlib inline' command in Jupyter Notebook for displaying the plot (quantifiable data: code demonstration and visualization process).", 'The chapter also demonstrates utilizing pandas to find the company with the highest average sales by grouping the company column and using the mean function, followed by deriving the summary statistics for the sales column using the describe function and applying the transpose function to flip the index with the column names (quantifiable data: highest average sales and summary statistics derivation).', 'The chapter emphasizes the importance of being able to quickly look at and describe data, regardless of the packages used, and concludes with a reminder to prepare for data analytics interview questions and encourages viewers to subscribe to the Simply Learn YouTube channel for similar content (quantifiable data: emphasis on data description and interview preparation).']}], 'duration': 405.353, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Y6175TGFuMI/pics/Y6175TGFuMI2581897.jpg', 'highlights': ['Demonstration of displaying the top and bottom customers based on profit in Tableau.', 'Guidance on generating random integers between 1 and 15 using NumPy.', 'Method for finding unique values for each column and subsetting data using pandas.', 'Heat maps visualize measures against dimensions using colors and size to compare one or more dimensions and up to two measures.', 'Tree maps use dimensions to define the structure and measure to define the size or color of individual rectangles.']}], 'highlights': ['Data wrangling involves cleaning, structuring, and enriching raw data for better decision making, with 80% of data analytics usually involving this step.', 'Best Practices for Data Cleaning focus on identifying and removing duplicates, maintaining data accuracy, and standardizing data at the point of entry to ensure an effective data analysis process. 80% of most data analysis is in cleaning the data.', 'Understanding the problem and interpreting the results are the most important steps in an analytics project.', 'Common problems encountered during analysis include handling duplicate and missing values, collecting meaningful data at the right time, ensuring data security, dealing with compliance issues, and handling data purging and storage problems.', 'Technical tools commonly used for analysis and presentation purposes include SQL Server, MySQL, Excel, SPSS, Tableau, and Python.', 'Handling Missing Values methods include listwise deletion, average imputation, regression substitution, and multiple imputation to fill in missing values, providing practical solutions for managing incomplete data sets.', 'Understanding Normal Distribution explains normal distribution as a symmetric continuous probability distribution, emphasizing the properties such as the bell curve appearance, equal mean, median, and mode, and the percentage of data within standard deviations from the mean.', 'Differences Between Joining and Blending in Tableau explains data joining and data blending in Tableau, highlighting the requirements and differences between the two methods for combining data from the same or different sources.', 'Overfitting vs Underfitting discusses the risks of overfitting in data analytics, where a model learns noise and random fluctuations too well, impacting performance, and contrasts it with underfitting, providing insight into the dangers of both phenomena.', 'Time Series Analysis defines time series analysis as a statistical method for analyzing ordered sequences of values at equally spaced time intervals, using COVID-19 cases as an example and highlighting its time-sensitive nature.', 'The chapter explains how to subset or filter data in SQL using WHERE and HAVING clauses. Explains the primary focus of the chapter.', "VLOOKUP syntax breakdown The VLOOKUP function in Excel is explained, involving the lookup value, table array, column index number, and range lookup, with an example of using VLOOKUP to retrieve data such as 'Prince' and 'Angela' from specified columns.", 'Filtering directors with an average movie duration greater than 115 minutes using the HAVING clause and aggregate functions. Illustrates the use of the HAVING clause and aggregate functions to filter directors with an average movie duration greater than 115 minutes.', 'Count functions in Excel The differences between count, count A, count blank, and count if functions in Excel are highlighted, demonstrating their distinct behaviors and providing quantifiable data such as the counts for each function.', 'Challenges of model training and generalization The challenges of model training and generalization are discussed, emphasizing poor performance on both train and test sets due to insufficient data for accurate model building and attempting to fit linear models to non-linear data.', 'Significance of Exploratory Data Analysis Exploratory Data Analysis is crucial for understanding data, building confidence for machine learning, refining feature selection, and uncovering hidden trends and insights, aiding in making actionable and profitable decisions.', 'Criteria for a Good Data Model A good data model should be intuitive, insightful, self-explanatory, easily consumed by clients, easily adaptable to changes, and scalable to new data, adhering to the KISS principle (Keep it Simple).', 'Types of Analytics Descriptive analytics provides insights into past events, predictive analytics forecasts future outcomes, and prescriptive analytics advises on potential actions, with examples related to sales analysis and addressing the COVID-19 pandemic.', 'Sampling Techniques in Data Analysis Sampling techniques include simple random sampling, systematic sampling, cluster sampling, stratified sampling, and judgmental or purposive sampling, offering varied approaches to gathering representative data subsets for analysis.', 'Types of Data Analysis Univariate analysis focuses on a single variable, bivariate analysis examines two variables for relationships and correlations, while multivariate analysis involves analyzing three or more variables to understand complex relationships and interdependencies.', 'Hypothesis Testing Hypothesis testing involves accepting or rejecting null and alternative hypotheses, exploring the presence or absence of relationships between predictor and outcome variables, providing a structured approach to statistical inference.', 'Treatment of Outliers Outliers in a dataset can be addressed by dropping them, capping their values, assigning them new values, or trying different transformations to mitigate their impact, ensuring data integrity and robust modeling.', 'Pandas data frame can be created by initializing a list or from a dictionary, allowing the designation of columns and index. Additionally, the Python code to create an employees data frame from the emp.csv file is demonstrated by importing the pandas library and using the readcsv function to load the csv file, with options to specify settings such as top index rows, columns, and skip rows.', 'Using the SUMIFS function in Excel to find total quantity sold and cost by sales reps', 'Creating dual axes charts in Tableau for sales and profits analysis', 'Using the union operator in SQL to combine results of select statements', 'Writing SQL query to find record with the fourth highest market price', 'Visualizing sales and profits by category using Tableau', 'Creating a pivot table in Tableau, including selecting table ranges and displaying cells as a percentage of the grand total', 'Constructing a SQL query to find products with total units sold greater than 1.5 million, involving inner join and group by', 'Importance of understanding inner join and group by in SQL queries, with a recommendation to break down complex queries', 'Demonstration of adding a column to a pandas data frame, involving assigning values to the new column and understanding the structure of data frames', 'Creating a stored procedure to find the sum of squares for the first four natural numbers, yielding a sum of 30', 'Writing a stored procedure to find the total even numbers between 30 and 45, resulting in 8 even numbers', 'Demonstration of displaying the top and bottom customers based on profit in Tableau.', 'Guidance on generating random integers between 1 and 15 using NumPy.', 'Method for finding unique values for each column and subsetting data using pandas.', 'Heat maps visualize measures against dimensions using colors and size to compare one or more dimensions and up to two measures.', 'Tree maps use dimensions to define the structure and measure to define the size or color of individual rectangles.']}