Overview
- Introduces Python specifically for advanced quantitative marketing and analytics
- Presents the concept of shareable reproducible research enabled by notebooks
- Applies Python to the building of statistical models using open source libraries such as sklearn and statsmodels
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Table of contents (12 chapters)
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Front Matter
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Basics of Python
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Front Matter
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Back Matter
Keywords
About this book
This book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas. Following the Python scientific computing movement toward reproducible research, the book presents all analyses in Colab notebooks, which integrate code, figures, tables, and annotation in a single file. The code notebooks for each chapter may be copied, adapted, and reused in one's own analyses. The book also introduces the usage of machine learning predictive models using the Python sklearn package in the context of marketing research.
This book is designed for three groups of readers: experienced marketing researchers who wish to learn to program in Python, coming from tools and languages such as R, SAS, or SPSS; analysts or students who already program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics.
Authors and Affiliations
About the authors
Jason Schwarz PhD is a Quantitative Researcher at Google and a former systems neurobiologist. His areas of research include perception, attention, motivation, behavioral pattern formation, and data visualization which he studies at scale at Google. Prior to joining Google, he was a data scientist at a startup where he ran analytics and developed and deployed production machine learning models on a Python stack.
Chris Chapman PhD is a Quantitative Researcher at Google, and an author of Chapman & Feit, R for Marketing Research and Analytics (Springer, 2015). In the broader industry, he has served as President of the American Marketing Association’s Practitioner Council, chaired the AMA Advanced Research Techniques Forum in 2012 and 2017, and is a member of several conference and industry committees. Chris regularly presents research innovations and teaches workshops on R, conjoint analysis, strategic modeling, and other analytics topics.
EleaMcDonnell Feit is an Assistant Professor of Marketing at Drexel University and a Senior Fellow of Marketing at The Wharton School. She enjoys making quantitative methods accessible to a broad audience and teaches workshops and courses on advertising measurement, marketing experiments, marketing analytics in R, discrete choice modeling and hierarchical Bayes methods. She is an author of Chapman & Feit, R for Marketing Research and Analytics (Springer, 2015).
Bibliographic Information
Book Title: Python for Marketing Research and Analytics
Authors: Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit
DOI: https://doi.org/10.1007/978-3-030-49720-0
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-49719-4Published: 03 November 2020
Softcover ISBN: 978-3-030-49722-4Published: 03 November 2021
eBook ISBN: 978-3-030-49720-0Published: 03 November 2020
Edition Number: 1
Number of Pages: XI, 272
Number of Illustrations: 11 b/w illustrations, 79 illustrations in colour
Topics: Statistics and Computing/Statistics Programs, Statistics for Business, Management, Economics, Finance, Insurance, Statistics for Social Sciences, Humanities, Law