Python for marketing research and analytics
著者
書誌事項
Python for marketing research and analytics
Springer, c2020
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注記
Includes bibliographical references (p. 265-266) and index
"This Springer imprint is published by the registered company Springer Nature Switzerland AG ... Cham, Switzerland"--T.p. verso
内容説明・目次
内容説明
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.
目次
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.
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