Introduction to Statistics in Spreadsheets
Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.
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Course Description
Statistics is the science that deals with the collection, analysis, and interpretation of data. Having a solid foundation in statistics will help you effectively work with your data to test hypotheses and uncover insights that can help solve your problems. This course is designed to give you that foundation in statistics. Using Spreadsheets functions, you'll dive into averages, distributions, hypothesis testing, and conclude the course by applying your newfound knowledge in a case study. Along the way, you'll work with a variety of datasets ranging from eBay auctions to train ridership to historical presidential approval ratings. Enjoy!
- 1
Getting To Know Your Data
FreeBegin your journey by learning why and how to summarize your data using statistics such as the mean, median, and mode. While working with a variety of datasets ranging from Amazon revenue to U.S Presidential ratings, you'll learn about the differences between each of these fundamental statistics and most importantly, when to use each.
Welcome to the course!50 xpDon't be average!100 xpPresidential approval rating averages100 xpDifference between median and mean100 xpModal madness100 xpHow far from average?50 xpTrain variation100 xpCalculating standard deviations100 xpPlaying quarters100 xpStandardizing data50 xpComparing z-scores100 xpExploring eBay auctions100 xp - 2
Statistical Data Visualization
Data visualization is one of the most important parts of any data science workflow. It leads to a deeper understanding of your dataset which in turn allows you to more effectively communicate results to stakeholders. In this chapter, you'll learn how to visualize your data in Spreadsheets using statistical plots such as the histogram, scatter plot, and bar plot.
Visualizing Distributions50 xp"Normal" views of money100 xpVisualizing customer longevity100 xpVisualizing customer donations100 xpIs the data "normally" distributed?100 xpVisualizing Correlations50 xpCorrelation between price and quantity sold100 xpCorrelation between seller rating and closing price100 xpAdding a trend line100 xpBar charts50 xpBar chart of competitive counts100 xpVisualizing categories100 xp - 3
Statistical Hypothesis Testing
This chapter introduces you to statistical hypothesis testing. You'll learn how to construct a hypothesis, test it using different statistical tests, and properly interpret the results.
Central to Stats: Sampling!50 xpSampling in Spreadsheets100 xpDoes sampling size matter?100 xpCentral Limit Theorem in action100 xpHypothesis Testing50 xpComparing samples with a t-test100 xpPaired t-test100 xpHypothesis Testing with the Z-test50 xpPerforming a Z-test100 xpWhat changes in a two-tailed test?100 xpHypothesis Testing with the Chi-squared test50 xpPerforming a chi-squared test100 xpAre bank loans getting worse?100 xp - 4
Case Study: Dating Profile Analysis
The final stretch! Apply all of your newfound statistical knowledge and solidify everything you have learned by working through a case study consisting of online dating profile data.
Dating Data!50 xpUnderstanding the distribution of ages100 xpWhat's the drinking age?100 xpProfile login behavior100 xpVisuals & Distributions50 xpVisualizing logins100 xpHow old do users look?100 xpTipping the scale to positive correlation50 xpInvestigating age and volunteering100 xpMore complex relationships50 xpAre gender and number of roommates independent?100 xpGetting old and rich100 xpMultiple relationships!100 xpCongratulations!50 xp
In the following tracks
Intermediate SpreadsheetsCollaborators
Prerequisites
Data Analysis in SpreadsheetsTed Kwartler
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Adjunct Professor, Harvard University
Ted Kwartler is the VP, Trusted AI at DataRobot. At DataRobot, Ted sets product strategy for explainable and ethical uses of data technology in the company's application. Ted brings unique insights and experience utilizing data, business acumen and ethics to his current and previous positions at Liberty Mutual Insurance and Amazon. In addition to having 4 DataCamp courses he teaches graduate courses at the Harvard Extension School and is the author of Text Mining in Practice with R.
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