Description

Book Synopsis

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.



Table of Contents
  • 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

Product Analytics

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A Paperback / softback by Joanne Rodrigues

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    Publisher: Pearson Education (US)
    Publication Date: 07/01/2021
    ISBN13: 9780135258521, 978-0135258521
    ISBN10: 0135258529
    Also in:
    Market research Computing Data warehousing

    Description

    Book Synopsis

    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.



    Table of Contents
    • 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

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