Applied Data Science Transforming User Data into Actionable Insights

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  • Edition: 1st
  • Format: Paperback
  • Copyright: 2019-05-20
  • Publisher: Addison-Wesley Professional
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This guide shows how to combine data science with social science to gain unprecedented insight into customer behavior, so you can change it. Joanne Rodrigues-Craig bridges the gap between predictive data science and statistical techniques that reveal why important things happen -- why customers buy more, or why they immediately leave your site -- so you can get more behaviors you want and less you don’t. 

Drawing on extensive enterprise experience and deep knowledge of demographics and sociology, Rodrigues-Craig shows how to create better theories and metrics, so you can accelerate the process of gaining insight, altering behavior, and earning business value. You’ll learn how to:
  • Develop complex, testable theories for understanding individual and social behavior in web products 
  • Think like a social scientist and contextualize individual behavior in today’s social environments 
  • Build more effective metrics and KPIs for any web product or system
  • Conduct more informative and actionable A/B tests 
  • Explore causal effects, reflecting a deeper understanding of the differences between correlation and causation
  • Alter user behavior in a complex web product 
  • Understand how relevant human behaviors develop, and the prerequisites for changing them
  • Choose the right statistical techniques for common tasks such as multistate and uplift modeling 
  • Use advanced statistical techniques to model multidimensional systems 
  • Do all of this in R (with sample code available in a separate code manual)

Table of Contents

Part I: The Basics
1 Data in Action
2. Building a Theory of the Universe
3. No Effect? Missteps in Metric Creation and Interpretation
4. Why Are My Users Leaving?
5. The Coveted Goal Post: How to Change Human Behavior

Part II: Advanced Analytics Tools
6. What Groups Should We Target?
7. Generating Insight
8. Which users will leave?
9. The Life of a Web Product
Part III: Applying R Statistical Tools

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