Python for Marketing Research and Analytics

✍ Scribed by Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit

Publisher: Springer International Publishing;Springer
Year: 2020
Tongue: English
Leaves: 273
Edition: 1st ed.
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xi
Front Matter ....Pages 1-1
Welcome to Python (Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit)....Pages 3-7
An Overview of Python (Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit)....Pages 9-45
Front Matter ....Pages 47-47
Describing Data (Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit)....Pages 49-75
Relationships Between Continuous Variables (Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit)....Pages 77-102
Comparing Groups: Tables and Visualizations (Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit)....Pages 103-120
Comparing Groups: Statistical Tests (Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit)....Pages 121-136
Identifying Drivers of Outcomes: Linear Models (Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit)....Pages 137-165
Additional Linear Modeling Topics (Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit)....Pages 167-192
Front Matter ....Pages 193-193
Reducing Data Complexity (Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit)....Pages 195-222
Segmentation: Unsupervised Clustering Methods for Exploring Subpopulations (Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit)....Pages 223-241
Classification: Assigning Observations to Known Categories (Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit)....Pages 243-261
Conclusion (Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit)....Pages 263-263
Back Matter ....Pages 265-272

✦ Subjects

Statistics; Statistics and Computing/Statistics Programs; Statistics for Business/Economics/Mathematical Finance/Insurance; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law

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