This how-to guide shows how to combine data science with social science to gain unprecedented insight into customer behavior, so you can change it.
Build KPIs to complement your data strategy and execute plans of action to leverage your data to achieve your business goals
Explore causal effects, reflecting a deeper understanding of the differences between correlation and causation
Conduct more informative and actionable A/B tests and alter user behavior in a complex web product
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.
*Reviews are from the Amazon and other seller websites
I’m very happy someone finally wrote a book on product analytics. This is a model everyone analyst should striRead More
Making sense out of massive data for product refinement with simple tools. This is what Joanne Rodrigues' goalRead More
Very insightful and easy to read and learn. I learned a lot from this book.
Insightful and useful.
Excellent book on the foundation of product analytics. wish the font were a little bigger though.
This book has some great insights for applied analytical questions, using a variety of techniques including caRead More
A how-to guide to maximize the usefulness of your data and generate actionable business insight.
Tracking the right KPIs is essential for data-driven decisions.
There a data errors that are made again and again in industry.
We must use conceptual tools to move from raw-data to actionable insight.
Avoiding common pitfalls in A/B testing is necessary to generate valuable insight.
To truly have insight we must answer the "why", we need tools and heuristics for causal inference.
Rather than a focus on theory, this book will give you tools to apply these algorithms to real-world business examples.
Resources to practice the techniques learned in the book & course materials for educatorsPractice Worksheet Book Data Full Course Manual
UCI Machine Learning : https://archive.ics.uci.edu/ml/datasets.php
Harvard University Dataverse: https://dataverse.harvard.edu
University of Michigan ICPSR: https://www.icpsr.umich.edu/web/ICPSR/search/studies
American Community Value Survey: https://www.ipums.org