9783030497194-3030497194-Python for Marketing Research and Analytics

Python for Marketing Research and Analytics

ISBN-13: 9783030497194
ISBN-10: 3030497194
Edition: 1st ed. 2020
Author: Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit
Publication date: 2020
Publisher: Springer
Format: Hardcover 283 pages
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Book details

ISBN-13: 9783030497194
ISBN-10: 3030497194
Edition: 1st ed. 2020
Author: Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit
Publication date: 2020
Publisher: Springer
Format: Hardcover 283 pages

Summary

Python for Marketing Research and Analytics (ISBN-13: 9783030497194 and ISBN-10: 3030497194), written by authors Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit, was published by Springer in 2020. With an overall rating of 3.5 stars, it's a notable title among other Statistics (Education & Reference) books. You can easily purchase or rent Python for Marketing Research and Analytics (Hardcover) from BooksRun, along with many other new and used Statistics books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $3.97.

Description

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. 

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