title
How I use Math as a Data Analyst

description
Statistics & Probability Course for Data Analysts 👉🏼https://lukeb.co/Statistics Shoutout to the real Math MVP 👉🏼 @Thuvu5 Certificates & Courses ================================== Coursera Courses: 📜 Google Data Analytics Certificate (START HERE) 👉🏼 https://lukeb.co/GoogleCert 💿 SQL for Data Science 👉🏼 https://lukeb.co/SQLdataScience 🧾 Excel Skills for Business 👉🏼  https://lukeb.co/ExcelBusinessAnalyst 🐍 Python for Everybody 👉🏼 https://lukeb.co/PythonForEverybody 📊 Data Visualization with Tableau 👉🏼 https://lukeb.co/Tableau_UCDavis 🏴‍☠️ Data Science: Foundations using R 👉🏼 https://lukeb.co/RforDataScienceJH Coursera Plus Subscription (7-day free trial) 👉🏼 https://lukeb.co/CourseraPlus 👨🏼‍🏫 All courses 👉🏼 https://kit.co/lukebarousse/data-analytics-courses Build a Portfolio Online ================================== 👩🏻‍💻Build portfolio here 👉🏼 http://hostinger.com/luke Rebate Code: "LUKE" My Portfolio 👉🏼 https://lukebarousse.tech/ Books for Data Nerds ================================== 📚 Books I’ve read 👉🏼 https://kit.co/lukebarousse/book-recommendations 📗 Data Science Must Read 👉🏼 https://geni.us/StorytellingWithData 📙 Tableau 👉🏼 https://geni.us/tableau 📘 Power BI👉🏼 https://geni.us/powerbi 📕 Python 👉🏼 https://geni.us/pythontricks Tech for Data Nerds ================================== ⚙️ Tech I use 👉🏼 https://kit.co/lukebarousse/computer-accessories 🪟Windows on a Mac (Parallels VM) 👉🏼 https://lukeb.co/ParallelsFreeTrial 👨🏼‍💻 M1 Macbook Air (Mac of choice) 👉🏼 https://geni.us/M1macAir8GB 💻 Dell XPS 13 (PC of choice) 👉🏼 https://geni.us/DellNewXPS13 💻 Asus Vivo Book (Lowest Cost PC) 👉🏼 https://geni.us/AsusVivoBook15 💻Lenovo IdeaPad (Best Value PC)👉🏼 https://geni.us/LenovoIdeaPad15 Social Media / Contact Me ====================== 🙋🏼‍♂️Newsletter: https://www.lukebarousse.com/ 🌄 Instagram: https://www.instagram.com/lukebarousse/ ⏰ TikTok: https://www.tiktok.com/@lukebarousse 📘 Facebook: https://www.facebook.com/datavizbyluke 📥 Business Inquiries: luke@lukebarousse.com 00:00 Intro 00:59 Arithmetic 01:33 Algebra 02:30 Descriptive Statistics 03:57 Probability Distributions 07:19 Regression - Advanced Statistics 09:07 Thu's Recommendations As a member of the Amazon, Coursera, Hostinger, Parallels, Interview Query, and Data Camp Affiliate Programs, I earn a commission from qualifying purchases on the links above. It costs you nothing but helps me with content creation. #datanerd #dataanalyst #datascience

detail
{'title': 'How I use Math as a Data Analyst', 'heatmap': [{'end': 112.785, 'start': 102.912, 'weight': 0.767}, {'end': 211.454, 'start': 195.365, 'weight': 0.719}, {'end': 470.132, 'start': 459.949, 'weight': 1}, {'end': 579.226, 'start': 556.9, 'weight': 0.846}], 'summary': 'Discusses essential math skills for data analysts, emphasizing continuous learning and recommending coursera for statistics. it covers the use of algebra and descriptive statistics in data analytics, the importance of using pivot tables, sql, and python for data analysis, and the significance of statistics and probability in data analysis, recommending an introduction to statistics course from stanford university on coursera. it also explores predicting data analyst salaries using regression, estimating an average salary of $73,000 for entry-level analysts, with an r-squared value of 0.11, indicating the need for further exploration and model refinement.', 'chapters': [{'end': 117.712, 'segs': [{'end': 24.505, 'src': 'embed', 'start': 0.009, 'weight': 0, 'content': [{'end': 5.553, 'text': "What up, data nerds? I'm Luke, a data analyst, and let's talk about how much math you need to know as a data analyst.", 'start': 0.009, 'duration': 5.544}, {'end': 9.595, 'text': "I've found, for my job, the majority of math can be categorized into four areas.", 'start': 5.633, 'duration': 3.962}, {'end': 13.378, 'text': 'The good news is, if you went to a secondary school, like a high school in the United States,', 'start': 9.695, 'duration': 3.683}, {'end': 15.839, 'text': 'you probably covered the majority of math that you need to know.', 'start': 13.378, 'duration': 2.461}, {'end': 17.52, 'text': 'So, you could probably turn this video off.', 'start': 16.02, 'duration': 1.5}, {'end': 18.561, 'text': 'That was a bad joke.', 'start': 17.901, 'duration': 0.66}, {'end': 19.082, 'text': "Don't leave.", 'start': 18.761, 'duration': 0.321}, {'end': 24.505, 'text': "As I'll be sharing how I use tools in my job to implement advanced math topics like probability and statistics.", 'start': 19.162, 'duration': 5.343}], 'summary': 'As a data analyst, most math needed is covered in high school, with advanced topics implemented using tools.', 'duration': 24.496, 'max_score': 0.009, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/tBfIh3VQX2o/pics/tBfIh3VQX2o9.jpg'}, {'end': 59.999, 'src': 'embed', 'start': 32.731, 'weight': 3, 'content': [{'end': 36.294, 'text': 'So I was super stoked when I reached out to Coursera and asked them if they would sponsor this video.', 'start': 32.731, 'duration': 3.563}, {'end': 37.355, 'text': "All right, so let's jump in.", 'start': 36.374, 'duration': 0.981}, {'end': 40.677, 'text': "We're gonna start with the basic concepts first and then move into the harder ones.", 'start': 37.375, 'duration': 3.302}, {'end': 45.141, 'text': "Let's say that I have a colleague, Tuvu, that comes to me with a work problem.", 'start': 40.858, 'duration': 4.283}, {'end': 45.922, 'text': 'Hey, Luke.', 'start': 45.441, 'duration': 0.481}, {'end': 46.522, 'text': 'Hey, Tu.', 'start': 46.202, 'duration': 0.32}, {'end': 51.046, 'text': "Hey, have you ever noticed what's odd? What? Every other number.", 'start': 47.103, 'duration': 3.943}, {'end': 59.098, 'text': 'Hey, can you jump on cleaning that data science job data that boss keeps bugging us about? Totes.', 'start': 53.635, 'duration': 5.463}, {'end': 59.999, 'text': 'Thanks, Luke.', 'start': 59.379, 'duration': 0.62}], 'summary': 'Coursera sponsored video on data science concepts and problem-solving.', 'duration': 27.268, 'max_score': 32.731, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/tBfIh3VQX2o/pics/tBfIh3VQX2o32731.jpg'}, {'end': 101.651, 'src': 'embed', 'start': 72.506, 'weight': 1, 'content': [{'end': 75.688, 'text': 'So for this, we have some data science job data that we want to clean up.', 'start': 72.506, 'duration': 3.182}, {'end': 78.09, 'text': 'Specifically, we want to calculate the total pay.', 'start': 75.808, 'duration': 2.282}, {'end': 84.255, 'text': 'Right now we have the base pay and the bonus pay, and we need to use basic addition of the two so we can get the total pay.', 'start': 78.61, 'duration': 5.645}, {'end': 93.003, 'text': 'We can then use some more advanced arithmetic to take it a step further and find the average total pay by summing up all those total pays and then dividing by the count of all those pays.', 'start': 84.275, 'duration': 8.728}, {'end': 96.686, 'text': 'Now, beyond arithmetic, algebra is the next most common type of math that I use.', 'start': 93.063, 'duration': 3.623}, {'end': 101.651, 'text': 'In this, you use unknown quantities along with known numbers to solve for missing values.', 'start': 96.866, 'duration': 4.785}], 'summary': 'Data science job data cleaned to calculate total pay and average total pay using arithmetic and algebra.', 'duration': 29.145, 'max_score': 72.506, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/tBfIh3VQX2o/pics/tBfIh3VQX2o72506.jpg'}, {'end': 137.266, 'src': 'heatmap', 'start': 102.912, 'weight': 2, 'content': [{'end': 104.314, 'text': 'Dead Nerds, Editor Luke here.', 'start': 102.912, 'duration': 1.402}, {'end': 107.258, 'text': "One quick note on the animations I've been using in this video so far.", 'start': 104.474, 'duration': 2.784}, {'end': 112.785, 'text': "I'm using them with the help of YouTuber 3Blue1Brown's Python library, which I have the code on over here.", 'start': 107.618, 'duration': 5.167}, {'end': 116.31, 'text': 'And so I want to give a quick shout out to the Manum team that developed this.', 'start': 113.466, 'duration': 2.844}, {'end': 117.712, 'text': 'All right, back to real Luke.', 'start': 116.47, 'duration': 1.242}, {'end': 123.256, 'text': "So in the case of this job data, let's say that we're trying to calculate the percentage of bonuses of the base bet.", 'start': 118.112, 'duration': 5.144}, {'end': 127.239, 'text': 'We would need to use algebra to rearrange this formula to solve for that bonus percentage.', 'start': 123.296, 'duration': 3.943}, {'end': 130.941, 'text': "Once it's rearranged, we can plug this into Excel and calculate that percentage.", 'start': 127.319, 'duration': 3.622}, {'end': 137.266, 'text': "And this basic math, along with a lot of other concepts that we'll cover today, can really be performed in any data analytics tool.", 'start': 131.042, 'duration': 6.224}], 'summary': "Using 3blue1brown's python library, luke covers job data analysis and algebra calculations for bonus percentages, applicable to any data analytics tool.", 'duration': 34.354, 'max_score': 102.912, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/tBfIh3VQX2o/pics/tBfIh3VQX2o102912.jpg'}], 'start': 0.009, 'title': 'Math for data analysts', 'summary': "Covers essential math skills for data analysts, including basic arithmetic and algebra. it emphasizes continuous learning and recommends coursera for statistics, as well as 3blue1brown's python library.", 'chapters': [{'end': 117.712, 'start': 0.009, 'title': 'Math for data analysts', 'summary': "Discusses the essential math skills required for data analysts, covering basic arithmetic and algebra, and emphasizes the importance of continuous learning, with a mention of using coursera for statistics, and a shout out to 3blue1brown's python library.", 'duration': 117.703, 'highlights': ['Luke categorizes the majority of math for data analysts into four areas, with emphasis on arithmetic and algebra, and mentions using Coursera for brushing up on Intro to Statistics.', 'Luke demonstrates the use of arithmetic for data cleaning by calculating total pay and finding the average total pay from given data science job data.', "Luke mentions using YouTuber 3Blue1Brown's Python library for animations in the video.", 'Luke mentions reaching out to Coursera for sponsorship of the video.']}], 'duration': 117.703, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/tBfIh3VQX2o/pics/tBfIh3VQX2o9.jpg', 'highlights': ['Luke categorizes math for data analysts into four areas, emphasizing arithmetic and algebra, and recommends Coursera for Intro to Statistics.', 'Luke demonstrates using arithmetic for data cleaning by calculating total pay and finding the average total pay from given data science job data.', "Luke mentions using 3Blue1Brown's Python library for animations in the video.", 'Luke mentions reaching out to Coursera for sponsorship of the video.']}, {'end': 353.965, 'segs': [{'end': 148.802, 'src': 'embed', 'start': 118.112, 'weight': 0, 'content': [{'end': 123.256, 'text': "So in the case of this job data, let's say that we're trying to calculate the percentage of bonuses of the base bet.", 'start': 118.112, 'duration': 5.144}, {'end': 127.239, 'text': 'We would need to use algebra to rearrange this formula to solve for that bonus percentage.', 'start': 123.296, 'duration': 3.943}, {'end': 130.941, 'text': "Once it's rearranged, we can plug this into Excel and calculate that percentage.", 'start': 127.319, 'duration': 3.622}, {'end': 137.266, 'text': "And this basic math, along with a lot of other concepts that we'll cover today, can really be performed in any data analytics tool.", 'start': 131.042, 'duration': 6.224}, {'end': 140.528, 'text': "So I'm using Excel now, but I'm going to go into other tools as well.", 'start': 137.386, 'duration': 3.142}, {'end': 143.511, 'text': 'Now, because I use arithmetic and algebra on a daily basis in my job,', 'start': 140.549, 'duration': 2.962}, {'end': 148.802, 'text': 'I really feel that most people have the skills necessary to start in data analytics.', 'start': 144.031, 'duration': 4.771}], 'summary': 'Using basic math and algebra to calculate bonus percentage, applicable in various data analytics tools.', 'duration': 30.69, 'max_score': 118.112, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/tBfIh3VQX2o/pics/tBfIh3VQX2o118112.jpg'}, {'end': 225.282, 'src': 'heatmap', 'start': 195.365, 'weight': 0.719, 'content': [{'end': 200.728, 'text': 'and can identify things like the average pay of a data analyst in general around 90,000, or,', 'start': 195.365, 'duration': 5.363}, {'end': 205.45, 'text': 'if we break it down further for entry-level data analysts, we can see that it is around 74,000..', 'start': 200.728, 'duration': 4.722}, {'end': 211.454, 'text': 'But honestly, when I get into larger data sets, I prefer to use tools like SQL, Power BI, or even Python.', 'start': 205.451, 'duration': 6.003}, {'end': 212.775, 'text': "Let's jump into SQL next.", 'start': 211.574, 'duration': 1.201}, {'end': 215.637, 'text': 'And for this, we have access to this job data in a database.', 'start': 212.915, 'duration': 2.722}, {'end': 219.019, 'text': 'I can easily write a SQL query to look at the different statistics I care about.', 'start': 215.697, 'duration': 3.322}, {'end': 225.282, 'text': 'And once again, I can even get to the average pay for a data analyst regardless of experience, at around 90,000.', 'start': 219.459, 'duration': 5.823}], 'summary': 'Average pay for data analysts: $90,000; entry-level: $74,000. tools used: sql, power bi, python.', 'duration': 29.917, 'max_score': 195.365, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/tBfIh3VQX2o/pics/tBfIh3VQX2o195365.jpg'}, {'end': 350.362, 'src': 'embed', 'start': 317.901, 'weight': 3, 'content': [{'end': 320.703, 'text': 'Now, box plots, also known as box and whisker plots,', 'start': 317.901, 'duration': 2.802}, {'end': 326.347, 'text': 'are great at showing the five most important attributes about a data set quickly and also visually.', 'start': 320.703, 'duration': 5.644}, {'end': 328.609, 'text': 'Take, for example, the total pay of data analysts.', 'start': 326.367, 'duration': 2.242}, {'end': 335.193, 'text': 'We can quickly find the median, minimum, and maximum, and also understand where the majority of the salaries reside within that 25 to 75 percentile.', 'start': 328.629, 'duration': 6.564}, {'end': 342.538, 'text': 'Now, because of the simplicity of this plot, I can more easily explore the job position level of data analysts.', 'start': 336.434, 'duration': 6.104}, {'end': 350.362, 'text': 'And we can see the median salary for entry level data analysts is around $70,000, where those for mid to senior level are around $100,000.', 'start': 342.558, 'duration': 7.804}], 'summary': 'Box plots visually display key data attributes, e.g. median, min, max, and salary percentiles, enabling quick analysis. for data analysts, median salary for entry level is $70,000, while mid to senior level is around $100,000.', 'duration': 32.461, 'max_score': 317.901, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/tBfIh3VQX2o/pics/tBfIh3VQX2o317901.jpg'}], 'start': 118.112, 'title': 'Data analytics and tools', 'summary': 'Covers the use of algebra and descriptive statistics in data analytics, the importance of using pivot tables, sql, and python for data analysis, and the usefulness of visualizations in understanding data trends and patterns.', 'chapters': [{'end': 188.26, 'start': 118.112, 'title': 'Data analytics and math skills', 'summary': 'Covers the use of algebra and descriptive statistics in data analytics, highlighting the ability to calculate bonus percentage using algebra and the importance of descriptive statistics in summarizing data, with a mention of using excel for calculations and moving into other data analytics tools, and a humorous reference to the reliability of statistics.', 'duration': 70.148, 'highlights': ['The use of algebra to calculate the percentage of bonuses from the base bet, demonstrating the practical application of math skills in data analytics.', 'The importance of descriptive statistics in summarizing data to provide generalities about the data and the limitation of our brains in comprehending large datasets, emphasizing the necessity of using such statistics.', 'The mention of using Excel for calculations and the plan to explore other data analytics tools, showcasing the versatility of applying mathematical concepts in various analytical platforms.', 'A humorous reference to the reliability of statistics, adding a lighthearted touch to the discussion about data analysis and math skills.']}, {'end': 353.965, 'start': 188.68, 'title': 'Data analysis tools and techniques', 'summary': 'Discusses the use of pivot tables, sql, and python for data analysis, providing insights into average pay for data analysts, probability distributions, and box plots, and highlighting the usefulness of visualizations in understanding data trends and patterns.', 'duration': 165.285, 'highlights': ['The chapter discusses the use of pivot tables, SQL, and Python for data analysis. Highlights the use of pivot tables, SQL, and Python as tools for data analysis.', 'Provides insights into average pay for data analysts, with the average pay being around $90,000 for all data analysts and $74,000 for entry-level data analysts. Reveals average pay statistics, indicating around $90,000 for all data analysts and around $74,000 for entry-level data analysts.', 'Discusses probability distributions and highlights the probability of a data analyst salary being above $85,000 with 50% probability, and above $72,800 with 75% probability. Explores probability distributions, indicating a 50% probability of salary being above $85,000 and a 75% probability of salary being above $72,800.', 'Highlights the usefulness of box plots in visualizing data trends and patterns, including insights into median salaries for entry-level and mid to senior level data analysts. Emphasizes the usefulness of box plots and provides insights into median salaries for entry-level and mid to senior level data analysts.']}], 'duration': 235.853, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/tBfIh3VQX2o/pics/tBfIh3VQX2o118112.jpg', 'highlights': ['The use of algebra to calculate the percentage of bonuses from the base bet, demonstrating the practical application of math skills in data analytics.', 'The importance of using pivot tables, SQL, and Python for data analysis, highlighting their significance as tools for data analysis.', 'The mention of using Excel for calculations and the plan to explore other data analytics tools, showcasing the versatility of applying mathematical concepts in various analytical platforms.', 'The usefulness of box plots in visualizing data trends and patterns, including insights into median salaries for entry-level and mid to senior level data analysts.']}, {'end': 600.028, 'segs': [{'end': 383.267, 'src': 'embed', 'start': 354.665, 'weight': 3, 'content': [{'end': 358.088, 'text': "And I don't really get into more than the basics of probabilities for my job.", 'start': 354.665, 'duration': 3.423}, {'end': 363.232, 'text': 'Someone like a data scientist may get more into exploring what distribution fits this data better.', 'start': 358.148, 'duration': 5.084}, {'end': 366.314, 'text': 'You know, they really should be using a Poisson distribution for this instead.', 'start': 363.312, 'duration': 3.002}, {'end': 373.239, 'text': "What's a croissant distribution? And I feel like understanding the basics of probability and statistics are good enough for my job.", 'start': 366.694, 'duration': 6.545}, {'end': 379.484, 'text': 'And this actually leads into the sponsor of this video, Coursera, and more specifically, the course I took to refresh my knowledge in all this.', 'start': 373.419, 'duration': 6.065}, {'end': 383.267, 'text': 'Working as a data analyst, I can comfortably say I use math on a daily basis.', 'start': 379.584, 'duration': 3.683}], 'summary': 'A data analyst emphasizes using basic probability and statistics for their job, with a nod to a course from coursera.', 'duration': 28.602, 'max_score': 354.665, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/tBfIh3VQX2o/pics/tBfIh3VQX2o354665.jpg'}, {'end': 430.016, 'src': 'embed', 'start': 401.12, 'weight': 0, 'content': [{'end': 406.183, 'text': 'Either way, in both scenarios, Having a core and basic understanding of mathematics is key for the job.', 'start': 401.12, 'duration': 5.063}, {'end': 406.983, 'text': 'Because of this,', 'start': 406.223, 'duration': 0.76}, {'end': 413.987, 'text': 'I think this Introduction to Statistics course from Stanford University on Coursera is not only great for veterans like me that want to refresh their knowledge,', 'start': 406.983, 'duration': 7.004}, {'end': 415.968, 'text': "but it's also great for beginners.", 'start': 413.987, 'duration': 1.981}, {'end': 424.413, 'text': 'It not only covers and goes into detail with statistics and probability, it also goes into a lot more reasoning and theory behind these mathematics.', 'start': 416.128, 'duration': 8.285}, {'end': 430.016, 'text': 'So you have a stronger foundation in not only explaining your work, but also defending your work to those math nerds.', 'start': 424.533, 'duration': 5.483}], 'summary': 'Introduction to statistics course from stanford university on coursera is beneficial for veterans and beginners, providing a strong foundation in mathematics.', 'duration': 28.896, 'max_score': 401.12, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/tBfIh3VQX2o/pics/tBfIh3VQX2o401120.jpg'}, {'end': 490.125, 'src': 'heatmap', 'start': 459.949, 'weight': 1, 'content': [{'end': 460.93, 'text': 'Oh, I regress.', 'start': 459.949, 'duration': 0.981}, {'end': 462.127, 'text': 'No two.', 'start': 461.747, 'duration': 0.38}, {'end': 464.769, 'text': "All right, so let's get into regression.", 'start': 462.167, 'duration': 2.602}, {'end': 470.132, 'text': 'This is a part of statistics that uses one or multiple variables to explain another variable.', 'start': 464.809, 'duration': 5.323}, {'end': 476.957, 'text': "So in our work case with two, let's try to use a variable to help explain or even predict what the total pay of data analysts should be.", 'start': 470.172, 'duration': 6.785}, {'end': 481.259, 'text': 'Years of experience seems to be one of the best starting points for using to predict this.', 'start': 477.037, 'duration': 4.222}, {'end': 485.722, 'text': 'From here we can use linear regression or, in the case of Power BI, a trend line,', 'start': 481.339, 'duration': 4.383}, {'end': 490.125, 'text': 'in order to get a predictor of what salaries should be based on years of experience.', 'start': 485.722, 'duration': 4.403}], 'summary': "Regression uses variables to predict total pay; experience is key. linear regression or power bi's trend line can predict salaries based on experience.", 'duration': 30.176, 'max_score': 459.949, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/tBfIh3VQX2o/pics/tBfIh3VQX2o459949.jpg'}, {'end': 531.437, 'src': 'embed', 'start': 503.891, 'weight': 1, 'content': [{'end': 506.452, 'text': 'with each average salary being around $73,000 for entry-level data analysts.', 'start': 503.891, 'duration': 2.561}, {'end': 510.696, 'text': "Now with these type of models, you're far from done here.", 'start': 508.393, 'duration': 2.303}, {'end': 512.477, 'text': 'You need to still take it a step further.', 'start': 510.776, 'duration': 1.701}, {'end': 515.821, 'text': 'In this case, I would look into metrics like R squared,', 'start': 512.538, 'duration': 3.283}, {'end': 520.785, 'text': 'which can help explain what percentage of the years of experience were explained by the movement in salary.', 'start': 515.821, 'duration': 4.964}, {'end': 526.973, 'text': 'Where one would be a perfect score and it can be directly explained and has a high correlation, and zero is quite the opposite,', 'start': 520.806, 'duration': 6.167}, {'end': 528.655, 'text': "and that it isn't explaining it very well.", 'start': 526.973, 'duration': 1.682}, {'end': 531.437, 'text': "Well, in the case of our model right here, it's actually not that good.", 'start': 528.735, 'duration': 2.702}], 'summary': "Entry-level data analysts earn around $73,000. model's r squared indicates poor performance.", 'duration': 27.546, 'max_score': 503.891, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/tBfIh3VQX2o/pics/tBfIh3VQX2o503891.jpg'}, {'end': 565.502, 'src': 'embed', 'start': 535.721, 'weight': 4, 'content': [{'end': 544.368, 'text': 'understanding how this R-squared correlated to this graph and then from there identify and explore new and different techniques to potentially model for this.', 'start': 535.721, 'duration': 8.647}, {'end': 546.109, 'text': 'Or maybe my data is just bad.', 'start': 544.528, 'duration': 1.581}, {'end': 551.774, 'text': "Please, no! No! Hey, Tu, so you've obviously had a lot of experience with data analytics.", 'start': 546.249, 'duration': 5.525}, {'end': 556.518, 'text': 'What would you say are areas that are important, or maybe not so important, for data analysts?', 'start': 552.334, 'duration': 4.184}, {'end': 565.502, 'text': "Generally, from my experience, I didn't really use a lot of calculus as a data analyst, mostly just enough to understand how linear regression works.", 'start': 556.9, 'duration': 8.602}], 'summary': 'Exploring new modeling techniques for data analytics, limited use of calculus.', 'duration': 29.781, 'max_score': 535.721, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/tBfIh3VQX2o/pics/tBfIh3VQX2o535721.jpg'}, {'end': 579.226, 'src': 'heatmap', 'start': 556.9, 'weight': 0.846, 'content': [{'end': 565.502, 'text': "Generally, from my experience, I didn't really use a lot of calculus as a data analyst, mostly just enough to understand how linear regression works.", 'start': 556.9, 'duration': 8.602}, {'end': 567.563, 'text': 'Also, not so much discrete math.', 'start': 565.642, 'duration': 1.921}, {'end': 572.624, 'text': "I think it's more relevant for people who do a lot of machine learning or software engineering.", 'start': 567.683, 'duration': 4.941}, {'end': 579.226, 'text': 'But I do use a lot of descriptive statistics and probability distribution almost on a day-to-day basis,', 'start': 572.664, 'duration': 6.562}], 'summary': 'Limited use of calculus and discrete math in data analysis; focuses on descriptive statistics and probability distribution.', 'duration': 22.326, 'max_score': 556.9, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/tBfIh3VQX2o/pics/tBfIh3VQX2o556900.jpg'}], 'start': 354.665, 'title': 'Importance of statistics in data analysis', 'summary': 'Emphasizes the significance of statistics and probability in data analysis, recommending an introduction to statistics course from stanford university on coursera. it also covers predicting data analyst salaries using regression, estimating an average salary of $73,000 for entry-level analysts, with an r-squared value of 0.11, indicating the need for further exploration and model refinement.', 'chapters': [{'end': 435.26, 'start': 354.665, 'title': 'Importance of statistics in data analysis', 'summary': 'Emphasizes the importance of having a core understanding of mathematics, particularly statistics and probability, in the field of data analysis, and recommends an introduction to statistics course from stanford university on coursera for both beginners and veterans, highlighting the practical application and theoretical understanding it offers.', 'duration': 80.595, 'highlights': ['The importance of a basic understanding of mathematics, particularly statistics and probability, is emphasized for data analysts, as it is crucial for daily tasks and for explaining and defending models and calculations to team members with varying levels of mathematical expertise.', 'The Introduction to Statistics course from Stanford University on Coursera is recommended for both beginners and experienced professionals, as it not only covers statistics and probability in detail, but also provides a stronger foundation in reasoning and theory behind the mathematics, enabling individuals to effectively explain and defend their work.']}, {'end': 600.028, 'start': 435.4, 'title': 'Predicting data analyst salaries with regression', 'summary': 'Covers the use of regression to predict data analyst salaries based on years of experience, achieving an average salary estimate of $73,000 for entry-level data analysts, but with a low r-squared value of 0.11, indicating the need for further exploration and potential model refinement.', 'duration': 164.628, 'highlights': ['Using linear regression to predict data analyst salaries, achieving an average estimate of $73,000 for entry-level data analysts and $117,000 for those with 10 years of experience.', 'Explaining the concept of R-squared as a metric to evaluate the percentage of years of experience explained by the movement in salary, with the model exhibiting a low R-squared value of 0.11, indicating the need for further exploration and potential model refinement.', 'Highlighting the importance of descriptive statistics and probability distribution for data analysts, emphasizing their role in exploring, summarizing, and understanding data on a day-to-day basis.', 'Noting the minimal relevance of calculus and discrete math for data analysts, as they are more pertinent to individuals involved in machine learning or software engineering.', "Acknowledging Tu's expertise in data analytics and her significant contribution to the video, while also recommending viewers to check out her video on mathematics and data science."]}], 'duration': 245.363, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/tBfIh3VQX2o/pics/tBfIh3VQX2o354665.jpg', 'highlights': ['The Introduction to Statistics course from Stanford University on Coursera is recommended for both beginners and experienced professionals, as it not only covers statistics and probability in detail, but also provides a stronger foundation in reasoning and theory behind the mathematics, enabling individuals to effectively explain and defend their work.', 'Using linear regression to predict data analyst salaries, achieving an average estimate of $73,000 for entry-level data analysts and $117,000 for those with 10 years of experience.', 'The importance of a basic understanding of mathematics, particularly statistics and probability, is emphasized for data analysts, as it is crucial for daily tasks and for explaining and defending models and calculations to team members with varying levels of mathematical expertise.', 'Highlighting the importance of descriptive statistics and probability distribution for data analysts, emphasizing their role in exploring, summarizing, and understanding data on a day-to-day basis.', 'Explaining the concept of R-squared as a metric to evaluate the percentage of years of experience explained by the movement in salary, with the model exhibiting a low R-squared value of 0.11, indicating the need for further exploration and potential model refinement.']}], 'highlights': ['The importance of using pivot tables, SQL, and Python for data analysis, highlighting their significance as tools for data analysis.', 'Using linear regression to predict data analyst salaries, achieving an average estimate of $73,000 for entry-level data analysts and $117,000 for those with 10 years of experience.', 'The Introduction to Statistics course from Stanford University on Coursera is recommended for both beginners and experienced professionals, as it not only covers statistics and probability in detail, but also provides a stronger foundation in reasoning and theory behind the mathematics, enabling individuals to effectively explain and defend their work.', 'The importance of a basic understanding of mathematics, particularly statistics and probability, is emphasized for data analysts, as it is crucial for daily tasks and for explaining and defending models and calculations to team members with varying levels of mathematical expertise.', 'Highlighting the importance of descriptive statistics and probability distribution for data analysts, emphasizing their role in exploring, summarizing, and understanding data on a day-to-day basis.']}