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- Develop complex, testable theories for understanding individual and social behavior in web products
- Think like a social scientist and contextualize individual behavior in today's social environments
- Build more effective metrics and KPIs for any web product or system
- Conduct more informative and actionable A/B tests
- Explore causal effects, reflecting a deeper understanding of the differences between correlation and causation
- Alter user behavior in a complex web product
- Understand how relevant human behaviors develop, and the prerequisites for changing them
- Choose the right statistical techniques for common tasks such as multistate and uplift modeling
- Use advanced statistical techniques to model multidimensional systems
- Do all of this in R (with sample code available in a separate code manual)
- Build better theories and metrics, and drive more of the behaviors you want
- Model, understand, and alter customer behavior to increase revenue and retention
- Construct better frameworks for examining why your customers do what they do
- Develop core metrics for user analytics, and conduct more effective A/B tests
- Master key techniques that most books ignore, including statistical matching and uplift modeling
- Use R and this book's many R examples to implement these techniques yourself
- Build better theories and metrics, and drive more of the behaviors you want
- Model, understand, and alter customer behavior to increase revenue and retention
- Construct better frameworks for examining why your customers do what they do
- Develop core metrics for user analytics, and conduct more effective A/B tests
- Master key techniques that most books ignore, including statistical matching and uplift modeling
- Use R and this book's many R examples to implement these techniques yourself
- Develop complex, testable theories for understanding individual and social behavior in web products
- Think like a social scientist and contextualize individual behavior in today's social environments
- Build more effective metrics and KPIs for any web product or system
- Conduct more informative and actionable A/B tests
- Explore causal effects, reflecting a deeper understanding of the differences between correlation and causation
- Alter user behavior in a complex web product
- Understand how relevant human behaviors develop, and the prerequisites for changing them
- Choose the right statistical techniques for common tasks such as multistate and uplift modeling
- Use advanced statistical techniques to model multidimensional systems
- Do all of this in R (with sample code available in a separate code manual)
- Build better theories and metrics, and drive more of the behaviors you want
- Model, understand, and alter customer behavior to increase revenue and retention
- Construct better frameworks for examining why your customers do what they do
- Develop core metrics for user analytics, and conduct more effective A/B tests
- Master key techniques that most books ignore, including statistical matching and uplift modeling
- Use R and this book's many R examples to implement these techniques yourself
- Build better theories and metrics, and drive more of the behaviors you want
- Model, understand, and alter customer behavior to increase revenue and retention
- Construct better frameworks for examining why your customers do what they do
- Develop core metrics for user analytics, and conduct more effective A/B tests
- Master key techniques that most books ignore, including statistical matching and uplift modeling
- Use R and this book's many R examples to implement these techniques yourself
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
| Erscheinungsjahr: | 2020 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: | Kartoniert / Broschiert |
| ISBN-13: | 9780135258521 |
| ISBN-10: | 0135258529 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Rodrigues, Joanne |
| Hersteller: | Pearson Education |
| Maße: | 231 x 180 x 25 mm |
| Von/Mit: | Joanne Rodrigues |
| Erscheinungsdatum: | 30.09.2020 |
| Gewicht: | 0,693 kg |
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
| Erscheinungsjahr: | 2020 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: | Kartoniert / Broschiert |
| ISBN-13: | 9780135258521 |
| ISBN-10: | 0135258529 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Rodrigues, Joanne |
| Hersteller: | Pearson Education |
| Maße: | 231 x 180 x 25 mm |
| Von/Mit: | Joanne Rodrigues |
| Erscheinungsdatum: | 30.09.2020 |
| Gewicht: | 0,693 kg |
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