Python for Marketing Research and Analytics 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 ones 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. . Part I: Basics of Python Chapter 1: Welcome to Python Chapter 2: The Python Language Part II Fundamentals of Data Analysis Chapter 3: Describing Data Chapter 4: Relationships Between Continuous Variables Chapter 5: Comparing Groups: Tables and Visualizations Chapter 6: Comparing Groups: Statistical Tests Chapter 7: Identifying Drivers of Outcomes: Linear Models Chapter 8: Additional Linear Modeling Topics Part III Advanced data analysis Chapter 9: Reducing Data Complexity Chapter 10: Segmentation: Unsupervised Clustering Methods for Exploring Subpopulations Chapter 11: Classification: Assigning observations to known categories Chapter 12: Conclusion Index.