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Behavioral Data Analysis with R and Python: Customer-Driven Data for Real Business Results - Softcover
Synopsis
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, this practical 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
"synopsis" may belong to another edition of this title.
About the Author
Florent Buisson is a behavioral economist with 10 years of experience in business, analytics, and behavioral science. He most recently started and led for four years the behavioral science team of Allstate Insurance Company.
Florent has published academic articles in journals such as the peer-reviewed Journal of Real Estate Research. He holds a Master's degree in econometrics as well as a Ph.D. in behavioral economics from the Sorbonne University in Paris.
"About this title" may belong to another edition of this title.
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Paperback. Condition: New. 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, this practical 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 dataExplore the differences between measurement and predictionLearn how to clean and prepare behavioral dataDesign and analyze experiments to drive optimal business decisionsUse behavioral data to understand and measure cause and effectSegment customers in a transparent and insightful way. Seller Inventory # LU-9781492061373
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