Product Analytics

Joanne Rodrigues is an experienced data scientist with master’s degrees in mathematics, political science, and demography. She has six years of experience in statistical computing and R programming, as well as experience with Python for data science applications. Her management experience at enterprise companies leverages her ability to understand human behavior by using economic and sociological theory in the context of complex mathematical models.

Part I: Qualitative Methodology
Chapter 1: Data in Action: A Model of a Dinner Party
Chapter 2: Building a Theory of the Universe–The Social Universe
Chapter 3: The Coveted Goal Post: How to Change User Behavior
Part II: Basic Statistical Methods
Chapter 4: Distributions in User Analytics
Chapter 5: Retained? Metric Creation and Interpretation
Chapter 6: Why Are My Users Leaving? The Ins and Outs of A/B Testing
Part III: Predictive Methods
Chapter 7: Modeling the User Space: k-Means and PCA
Chapter 8: Predicting User Behavior: Regression, Decision Trees, and Support Vector Machines
Chapter 9: Forecasting Population Changes in Product: Demographic Projections
Part IV: Causal Inference Methods
Chapter 10: In Pursuit of the Experiment: Natural Experiments and the Difference-in-Difference Design
Chapter 11: In Pursuit of the Experiment Continued: Regression Discontinuity, Time Series Modelling, and Interrupted Time Series Approaches
Chapter 12: Developing Heuristics in Practice: Statistical Matching and Hill’s Causality Conditions
Chapter 13: Uplift Modeling
Part V: Basic, Predictive, and Causal Inference Methods in R
Chapter 14: Metrics in R
Chapter 15: A/B Testing, Predictive Modeling, and Population Projection in R
Chapter 16: Regression Discontinuity, Matching, and Uplift in R
Conclusion