Python for Marketing Research and Analytics

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.


Author: Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit
Publisher: Springer
Published: 11/03/2021
Pages: 272
Binding Type: Paperback
Weight: 1.43lbs
Size: 11.00h x 8.25w x 0.60d
ISBN13: 9783030497224
ISBN10: 3030497224
BISAC Categories:
- Computers | Mathematical & Statistical Software
- Business & Economics | Statistics
- Social Science | Statistics

About the Author

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.

Elea McDonnell 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).

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