| 書目名稱 | Python for Marketing Research and Analytics | | 編輯 | Jason S. Schwarz,Chris Chapman,Elea McDonnell Feit | | 視頻video | http://file.papertrans.cn/765/764246/764246.mp4 | | 概述 | Introduces Python specifically for advanced quantitative marketing and analytics.Presents the concept of shareable reproducible research enabled by notebooks.Applies Python to the building of statisti | | 圖書封面 |  | | 描述 | .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.?. | | 出版日期 | Book 2020 | | 關(guān)鍵詞 | python; statistics; marketing research; data science; econometrics; machine learning | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-49720-0 | | isbn_softcover | 978-3-030-49722-4 | | isbn_ebook | 978-3-030-49720-0 | | copyright | Springer Nature Switzerland AG 2020 |
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