If you’re analyzing data in a business with R or Python, this book is for you. I use the word “business” loosely to mean any for-profit, nonprofit, or governmental organiza‐ tion where correct insights and actionable conclusions driving action are what matters.
In terms of math and stats background, it doesn’t matter whether you are a business analyst building monthly forecasts, a UX researcher looking at click-through behav‐ iors, or a data scientist building machine learning models. This book has one funda‐ mental prerequisite: you need to be at least somewhat familiar with linear and logistic regression. If you understand regression, you can follow the argument of this book and reap great benefits from it. On the other side of the spectrum, I believe even expert data scientists with PhDs in statistics or computer science will find the material new and useful, provided they are not already specialists in behavioral or causal analytics.
In terms of programming background, you need to be able to read and write code in R or Python, ideally both. I will not show you how to define a function or how to manipulate data structures such as data frames or pandas. There are already excellent books doing a better job of it than I would (e.g., Python for Data Analysis by Wes McKinney (O’Reilly) and R for Data Science by Garrett Grolemund and Hadley Wick‐ ham (O’Reilly)). If you’ve read any of these books, taken an introductory class, or used at least one of the two languages at work, then you’ll be equipped for the mate‐ rial here. Similarly, I will usually not present and discuss the code used to create the numerous figures in the book, although it will be in the book’s GitHub.
If you’re in academia or a field that requires you to follow academic norms (e.g., pharmaceutical trials), this book might still be of interest to you—but the recipes I’m describing might get you in trouble with your advisor/editor/manager.
This book is not an overview of conventional behavioral data analysis methods, such as T-test or ANOVA. I have yet to encounter a situation where regression was less effective than these methods for providing an answer to a business question, which is why I’m deliberately restraining this book to linear and logistic regression. If you want to learn other methods, you’ll have to look elsewhere (e.g., Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (O’Reilly) by Aurélien Géron for machine learning algorithms).
Understanding and changing behaviors in applied settings requires both data analysis and qualitative skills. This book focuses squarely on the former, primarily for reasons of space. In addition, there are already excellent books that cover the latter, such as Nudge: Improving Decisions About Health, Wealth, and Happiness (Penguin) by Richard Thaler and Cass Sunstein and Designing for Behavior Change: Applying Psy‐ chology and Behavioral Economics (O’Reilly) by Stephen Wendel. Nonetheless, I’ll provide an introduction to behavioral science concepts so that you can apply the tools from this book even if you’re new to the field.
Finally, if you’re completely new to data analysis in R or Python, this is not the book for you. I recommend starting with some of the excellent introductions out there, such as the ones mentioned in this section.
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