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Python for marketing research and an...

Chapman, Chris.

Python for marketing research and analytics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Python for marketing research and analyticsby Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit.
Author:	Schwarz, Jason S.
other author:	Chapman, Chris.
Published:	Cham :Springer International Publishing :2020.
Description:	xi, 272 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	MarketingData processing.
Online resource:	https://doi.org/10.1007/978-3-030-49720-0
ISBN:	9783030497200$q(electronic bk.)

Python for marketing research and analytics
Schwarz, Jason S.

Python for marketing research and analytics[electronic resource] /by Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit. - Cham :Springer International Publishing :2020. - xi, 272 p. :ill. (some col.), digital ;24 cm.

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.

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.

ISBN: 9783030497200$q(electronic bk.)

Standard No.: 10.1007/978-3-030-49720-0doiSubjects--Topical Terms:

221562
Marketing
--Data processing.

LC Class. No.: HF5415.125

Dewey Class. No.: 658.830285

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505 0 $a 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.
520 $a 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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