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 stri
Read MoreMaking sense out of massive data for product refinement with simple tools. This is what Joanne Rodrigues' goal
Read MoreVery 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 ca
Read MoreA 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 educators
Practice 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
My passion is to analyze large amounts of real-world behavioral data structured, semi-structured or unstructured to generate actionable business insights.
I am an experienced data scientist and enterprise manager with a masters' degree in mathematics (London School of Economics), political science (University of California, Berkeley), and demography (University of California, Berkeley), and a bachelor's degree in international economics (Georgetown University).
She pioneered new techniques at Sony Playstation, led all of MeYou Health's data science efforts, and founded a company ClinicPriceCheck.com, featured on TechCrunch Battlefield SF 2020.
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A how-to guide for how to apply your product data to making business decision. This book is essential for those who use data to make impactful business decisions.
Common pitfalls to data analysis and how we should think about social behavior.
How we should approach metric development and understanding customer behavior.
Understanding theories of human behavior change
A brief overview of statistical concepts that for product analytics
A deep-dive into the process of metric creation and common metrics in product analytics
An overview of important concepts of correlation and causation and a guide to setting up A/B tests
A simple, introductory quide to unsupervised learning in product analytics
An introductory guide to supervised learning techniques in product analytics
An introductory guide to demographic poplation projection techniques in product analytics
An introduction to natural experiments and difference-in-difference modeling in a product analytics context
An introduction to regression discontinuity and interrupted time series designs
An introduction to statistical matching and causal inference heuristics
An introduction to uplift modeling for A/B testing results
A guide to building your defined metrics
A guide to running A/B testing, predictive modeling and population projection in R
An example of an RD design, matching and uplight example in R
Product Analytics for Data-Driven Decisions: Derive Insights from Web Analytics Data explores core concepts that will help viewers work with their data, identify bias in data sets, differentiate good data from bad data, and ultimately derive insights to help make actionable business decisions. Learners will see real-world examples of successful product analytics and learn how to utilize qualitative and quantitative measures for desirable outcomes.
Simple, visual explanations.
Acheive your product goals with effective KPIs, A/B testing, and data analysis