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The most successful companies today are the ones that know their customers so well that they can anticipate their needs. Customer analytics and in particular A/B Testing are crucial parts of leveraging quantitative know-how to help make business decisions that generate value. This course covers the ins and outs of how to use Python to analyze customer behavior and business trends as well as how to create, run, and analyze A/B tests to make proactive, data-driven business decisions.
Key Performance Indicators: Measuring Business SuccessFree
This chapter provides a brief introduction to the content that will be covered throughout the course before transitioning into a discussion of Key Performance Indicators or KPIs. You'll learn how to identify and define meaningful KPIs through a combination of critical thinking and leveraging Python tools. These techniques are all presented in a highly practical and generalizable way. Ultimately these topics serve as the core foundation for the A/B testing discussion that follows.Course introduction and overview50 xpUnderstanding the key components of an A/B test50 xpDefining meaningful KPIs50 xpIdentifying and understanding KPIs50 xpLoading & examining our data100 xpMerging on different sets of fields100 xpExploratory analysis of KPIs50 xpPracticing aggregations100 xpGrouping & aggregating100 xpCalculating KPIs - a practical example50 xpCalculating KPIs100 xpAverage purchase price by cohort100 xp
Exploring and Visualizing Customer Behavior
This chapter teaches you how to visualize, manipulate, and explore KPIs as they change over time. Through a variety of examples, you'll learn how to work with datetime objects to calculate metrics per unit time. Then we move to the techniques for how to graph different segments of data, and apply various smoothing functions to reveal hidden trends. Finally we walk through a complete example of how to pinpoint issues through exploratory data analysis of customer data. Throughout this chapter various functions are introduced and explained in a highly generalizable way.Working with time series data in pandas50 xpParsing dates100 xpCreating time series graphs with Matplotlib50 xpPlotting time series data100 xpPivoting our data100 xpExamining the different cohorts100 xpUnderstanding and visualizing trends50 xpSeasonality and moving averages100 xpExponential rolling average & over/under smoothing100 xpEvents and releases50 xpVisualizing user spending100 xpLooking more closely at revenue50 xp
The Design and Application of A/B Testing
In this chapter you will dive fully into A/B testing. You will learn the mathematics and knowledge needed to design and successfully plan an A/B test from determining an experimental unit to finding how large a sample size is needed. Accompanying this will be an introduction to the functions and code needed to calculate the various quantities associated with a statistical test of this type.Introduction to A/B testing50 xpGood applications of A/B testing50 xpGeneral properties of an A/B Test50 xpA/B test generalizability50 xpInitial A/B test design50 xpExperimental units: Revenue per user day100 xpPreparing to run an A/B test50 xpConversion rate sensitivities100 xpSensitivity100 xpStandard error100 xpCalculating sample size50 xpExploring the power calculation100 xpCalculating the sample size100 xp
Analyzing A/B Testing Results
After running an A/B test, you must analyze the data and then effectively communicate the results. This chapter begins by interleaving the theory of statistical significance and confidence intervals with the tools you need to calculate them yourself from the data. Next we discuss how to effectively visualize and communicate these results. This chapter is the culmination of all the knowledge built over the entire course.Analyzing the A/B test results50 xpConfirming our test results100 xpThinking critically about p-values50 xpUnderstanding statistical significance50 xpIntuition behind statistical significance100 xpChecking for statistical significance100 xpUnderstanding confidence intervals100 xpCalculating confidence intervals100 xpInterpreting your test results50 xpPlotting the distribution100 xpPlotting the difference distribution100 xpFinale50 xp
In the following tracksMarketing Analytics with Python
DatasetsCustomer datasetIn-App Purchases datasetDaily Revenue datasetUser Demographics Paywall datasetAB Testing Results
Data Scientist at EDO Inc.
Ryan is a Data Scientist at EDO Inc, a Data Science Software company. Prior to that he worked designing and analyzing A/B tests and customer data as a member of the Business Analytics team at Tinder. He received his bachelor's degree in Statistics from Harvard University and is passionate about leverage data analytics to improve customer experiences and help companies run more efficiently.