Articoli correlati a Python for Marketing Research and Analytics
- EditoreSpringer-Nature New York Inc
- Data di pubblicazione2020
- ISBN 10 3030497194
- ISBN 13 9783030497194
- RilegaturaCopertina rigida
- Numero edizione1
- Numero di pagine284
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Python for Marketing Research and Analytics
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Springer International Publishing Nov 2020
(2020)
ISBN 10: 3030497194
ISBN 13: 9783030497194
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Descrizione libro Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 not Elektronisches Buch, which integrate code, figures, tables, and annotation in a single file. The code not Elektronisches Buch 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. 284 pp. Englisch. Codice articolo 9783030497194
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Python for Marketing Research and Analytics
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Springer International Publishing
(2020)
ISBN 10: 3030497194
ISBN 13: 9783030497194
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Descrizione libro Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 not Elektronisches Buch, which integrate code, figures, tables, and annotation in a single file. The code not Elektronisches Buch 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. Codice articolo 9783030497194
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Python for Marketing Research and Analytics
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ISBN 13: 9783030497194
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Descrizione libro Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces Python specifically for advanced quantitative marketing and analytics Presents the concept of shareable reproducible research enabled by notebooksApplies Python to the building of statistical models using open source librari. Codice articolo 448683426
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Descrizione libro Hardcover. Condizione: Brand New. 272 pages. 9.25x6.10x0.87 inches. In Stock. Codice articolo zk3030497194
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Python for Marketing Research and Analytics
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Springer
(2020)
ISBN 10: 3030497194
ISBN 13: 9783030497194
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Descrizione libro Hardcover. Condizione: New. First Edition. First Edition thus, as good as new. Python for Marketing Research and Analytics by Jason S. Schwarz; Chris Chapman; Elea McDonnell Feit. Published by Springer in 2020. Hardcover ISBN:9783030497194. Collectible item in excellent condition. Codice articolo 3030497194
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