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1 |
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|a Schwarz, Jason S.,
|e author.
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| 245 |
1 |
0 |
|a Python for marketing research and analytics /
|c Jason S. Schwarz, Chris Chapman, Elea McDonnell Feit.
|
| 264 |
|
1 |
|a Cham, Switzerland :
|b Springer,
|c [2020]
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| 300 |
|
|
|a 1 online resource (xi, 272 pages) :
|b illustrations (some color)
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| 504 |
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|a Includes bibliographical references and 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.
|
| 505 |
0 |
|
|a Basics of Python. Welcome to Python -- An overview of Python -- Fundamentals of data analysis. Describing data -- Relationships between continuous variables -- Comparing groups : tables and visualizations -- Comparing groups : statistical tests -- Identifying drivers of outcomes : linear models --Additional linear modeling topics -- Advanced data analysis. Reducing data complexity -- Segmentation : unsupervised clustering methods for exploring subpopulations -- Classification : assigning observations to known categories.
|
| 588 |
0 |
|
|a Online resource; title from digital title page (ProQuest Ebook Central, viewed August 27, 2021).
|
| 650 |
|
0 |
|a Marketing
|x Data processing.
|
| 650 |
|
0 |
|a Marketing research.
|
| 650 |
|
0 |
|a Python (Computer program language)
|
| 650 |
|
0 |
|a Information visualization.
|
| 650 |
|
7 |
|a Python (Lenguaje de programación)
|2 embne
|
| 650 |
|
7 |
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|x Datos-Tratamiento
|2 embne
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| 650 |
|
7 |
|a Information visualization
|2 fast
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| 650 |
|
7 |
|a Marketing
|x Data processing
|2 fast
|
| 650 |
|
7 |
|a Marketing research
|2 fast
|
| 650 |
|
7 |
|a Python (Computer program language)
|2 fast
|
| 655 |
|
0 |
|a Electronic books.
|
| 700 |
1 |
|
|a Chapman, Chris
|c (Data analyst),
|e author.
|1 https://id.oclc.org/worldcat/entity/E39PCjFvMMJHwHgkKJqRpqxVqP
|
| 700 |
1 |
|
|a Feit, Elea McDonnell,
|d 1973-
|e author.
|1 https://id.oclc.org/worldcat/entity/E39PCjKW9mpMcq7TmJq6whjWfy
|
| 758 |
|
|
|i has work:
|a Python for marketing research and analytics (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCFJJHgQBgBvTXvRQJY86cd
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a Schwarz, Jason S.
|t Python for marketing research and analytics.
|d Cham, Switzerland : Springer, [2020]
|z 9783030497194
|w (OCoLC)1154100704
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-49720-0
|y Click for online access
|
| 903 |
|
|
|a SPRING-MATH2020
|
| 994 |
|
|
|a 92
|b HCD
|