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 MethodologyChapter 1: Data in Action: A Model of a Dinner PartyChapter 2: Building a Theory of the Universe–The Social UniverseChapter 3: The Coveted Goal Post: How to Change User BehaviorPart II: Basic Statistical MethodsChapter 4: Distributions in User AnalyticsChapter 5: Retained? Metric Creation and InterpretationChapter 6: Why Are My Users Leaving? The Ins and Outs of A/B TestingPart III: Predictive MethodsChapter 7: Modeling the User Space: k-Means and PCAChapter 8: Predicting User Behavior: Regression, Decision Trees, and Support Vector MachinesChapter 9: Forecasting Population Changes in Product: Demographic ProjectionsPart IV: Causal Inference MethodsChapter 10: In Pursuit of the Experiment: Natural Experiments and the Difference-in-Difference DesignChapter 11: In Pursuit of the Experiment Continued: Regression Discontinuity, Time Series Modelling, and Interrupted Time Series ApproachesChapter 12: Developing Heuristics in Practice: Statistical Matching and Hill’s Causality ConditionsChapter 13: Uplift ModelingPart V: Basic, Predictive, and Causal Inference Methods in RChapter 14: Metrics in RChapter 15: A/B Testing, Predictive Modeling, and Population Projection in RChapter 16: Regression Discontinuity, Matching, and Uplift in RConclusion

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