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Behavioural Data Analysis with R and Python

Customer-Driven Data for Real Business Results

Paperback Engels 2021 9781492061373
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Samenvatting

Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis. Common data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as any other data. Instead, this practical guide introduces powerful methods specifically tailored for behavioral data analysis.

Advanced experimental design helps you get the most out of your A/B tests, while causal diagrams allow you to tease out the causes of behaviors even when you can't run experiments. Written in an accessible style for data scientists, business analysts, and behavioral scientists, thispractical book provides complete examples and exercises in R and Python to help you gain more insight from your data-immediately.

- Understand the specifics of behavioral data
- Explore the differences between measurement and prediction
- Learn how to clean and prepare behavioral data
- Design and analyze experiments to drive optimal business decisions
- Use behavioral data to understand and measure cause and effect
- Segment customers in a transparent and insightful way

Specificaties

ISBN13:9781492061373
Taal:Engels
Bindwijze:paperback
Aantal pagina's:300
Uitgever:O'Reilly
Druk:1
Verschijningsdatum:29-6-2021
Hoofdrubriek:IT-management / ICT
ISSN:

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Inhoudsopgave

Preface
Who This Book Is For
Who This Book Is Not For
R and Python Code
Code Environments
Code Conventions
Functional-Style Programming 101
Using Code Examples
Navigating This Book
Conventions Used in This Book
O’Reilly Online Learning
How to Contact Us
Acknowledgments

I. Understanding Behaviors
1. The Causal-Behavioral Framework for Data Analysis
Why We Need Causal Analytics to Explain Human Behavior
The Different Types of Analytics
Human Beings Are Complicated
Confound It! The Hidden Dangers of Letting Regression Sort It Out
Data
Why Correlation Is Not Causation: A Confounder in Action
Too Many Variables Can Spoil the Broth
Conclusion

2. Understanding Behavioral Data
A Basic Model of Human Behavior
Personal Characteristics
Cognition and Emotions
Intentions
Actions
Business Behaviors
How to Connect Behaviors and Data
Develop a Behavioral Integrity Mindset
Distrust and Verify
Identify the Category
Refine Behavioral Variables
Understand the Context
Conclusion

II. Causal Diagrams and Deconfounding
3. Introduction to Causal Diagrams
Causal Diagrams and the Causal-Behavioral Framework
Causal Diagrams Represent Behaviors
Causal Diagrams Represent Data
Fundamental Structures of Causal Diagrams
Chains
Forks
Colliders
Common Transformations of Causal Diagrams
Slicing/Disaggregating Variables
Aggregating Variables
What About Cycles?
Paths
Conclusion

4. Building Causal Diagrams from Scratch
Business Problem and Data Setup
Data and Packages
Understanding the Relationship of Interest
Identify Candidate Variables to Include
Actions
Intentions
Cognition and Emotions
Personal Characteristics
Business Behaviors
Time Trends
Validate Observable Variables to Include Based on Data
Relationships Between Numeric Variables
Relationships Between Categorical Variables
Relationships Between Numeric and Categorical Variables
Expand Causal Diagram Iteratively
Identify Proxies for Unobserved Variables
Identify Further Causes
Iterate
Simplify Causal Diagram
Conclusion

5. Using Causal Diagrams to Deconfound Data Analyses
Business Problem: Ice Cream and Bottled Water Sales
The Disjunctive Cause Criterion
Definition
First Block
Second Block
The Backdoor Criterion
Definitions
First Block
Second Block
Conclusion

III. Robust Data Analysis
6. Handling Missing Data
Data and Packages
Visualizing Missing Data
Amount of Missing Data
Correlation of Missingness
Diagnosing Missing Data
Causes of Missingness: Rubin’s Classification
Diagnosing MCAR Variables
Diagnosing MAR Variables
Diagnosing MNAR Variables
Missingness as a Spectrum
Handling Missing Data
Introduction to Multiple Imputation (MI)
Default Imputation Method: Predictive Mean Matching
From PMM to Normal Imputation (R Only)
Adding Auxiliary Variables
Scaling Up the Number of Imputed Data Sets
Conclusion

7. Measuring Uncertainty with the Bootstrap
Intro to the Bootstrap: “Polling” Oneself Up
Packages
The Business Problem: Small Data with an Outlier
Bootstrap Confidence Interval for the Sample Mean
Bootstrap Confidence Intervals for Ad Hoc Statistics
The Bootstrap for Regression Analysis
When to Use the Bootstrap
Conditions for the Traditional Central Estimate to Be Sufficient
Conditions for the Traditional CI to Be Sufficient
Determining the Number of Bootstrap Samples
Optimizing the Bootstrap in R and Python
R: The boot Package
Python Optimization
Conclusion

IV. Designing and Analyzing Experiments
8. Experimental Design: The Basics
Planning the Experiment: Theory of Change
Business Goal and Target Metric
Intervention
Behavioral Logic
Data and Packages
Determining Random Assignment and Sample Size/Power
Random Assignment
Sample Size and Power Analysis
Analyzing and Interpreting Experimental Results
Conclusion

9. Stratified Randomization
Planning the Experiment
Business Goal and Target Metric
Definition of the Intervention
Behavioral Logic
Data and Packages
Determining Random Assignment and Sample Size/Power
Random Assignment
Power Analysis with Bootstrap Simulations
Analyzing and Interpreting Experimental Results
Intention-to-Treat Estimate for Encouragement Intervention
Complier Average Causal Estimate for Mandatory Intervention
Conclusion

10. Cluster Randomization and Hierarchical Modeling
Planning the Experiment
Business Goal and Target Metric
Definition of the Intervention
Behavioral Logic
Data and Packages
Introduction to Hierarchical Modeling
R Code
Python Code
Determining Random Assignment and Sample Size/Power
Random Assignment
Power Analysis
Analyzing the Experiment
Conclusion
V. Advanced Tools in Behavioral Data Analysis

11. Introduction to Moderation
Data and Packages
Behavioral Varieties of Moderation
Segmentation
Interactions
Nonlinearities
How to Apply Moderation
When to Look for Moderation?
Multiple Moderators
Validating Moderation with Bootstrap
Interpreting Individual Coefficients
Conclusion

12. Mediation and Instrumental Variables
Mediation
Understanding Causal Mechanisms
Causal Biases
Identifying Mediation
Measuring Mediation
Instrumental Variables
Data
Packages
Understanding and Applying IVs
Measurement
Applying IVs: Frequently Asked Questions
Conclusion
Bibliography

Index

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