Python for Marketing Research and Analytics
暫譯: Python 在行銷研究與分析中的應用

Schwarz, Jason S., Chapman, Chris, Feit, Elea McDonnell

Python for Marketing Research and Analytics
  • 出版商: Springer
  • 出版日期: 2020-11-03
  • 售價: $3,150
  • 貴賓價: 9.5 折 $2,993
  • 語言: 英文
  • 頁數: 272
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3030497194
  • ISBN-13: 9783030497194
  • 相關分類: Python、程式語言、行銷/網路行銷 Marketing

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

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.

商品描述(中文翻譯)

這本書提供了使用 Python 進行定量行銷的介紹。書中採用實作的方法來解決真實的行銷問題，並根據關鍵主題區域進行組織。隨著 Python 科學計算運動朝向可重現研究的發展，書中所有的分析都在 Colab 筆記本中呈現，這些筆記本將程式碼、圖形、表格和註解整合在一個文件中。每一章的程式碼筆記本可以被複製、調整和重用於自己的分析中。書中還介紹了在行銷研究背景下使用 Python 的 sklearn 套件進行機器學習預測模型的用法。

這本書的讀者群體分為三類：希望學習 Python 程式設計的經驗豐富的行銷研究者，他們來自 R、SAS 或 SPSS 等工具和語言；已經會使用 Python 程式設計的分析師或學生，並希望了解行銷應用；以及幾乎沒有程式設計背景的本科或研究生行銷學生。書中僅假設讀者對正式統計學有初步的了解，並包含最少的數學內容。

作者簡介

Jason Schwarz PhD is a Quantitative Researcher at Google and a former systems neurobiologist. His areas of research include perception, attention, motivation, behavioral pattern formation, and data visualization which he studies at scale at Google. Prior to joining Google, he was a data scientist at a startup where he ran analytics and developed and deployed production machine learning models on a Python stack.

Chris Chapman PhD is a Quantitative Researcher at Google, and an author of Chapman & Feit, R for Marketing Research and Analytics (Springer, 2015). In the broader industry, he has served as President of the American Marketing Association's Practitioner Council, chaired the AMA Advanced Research Techniques Forum in 2012 and 2017, and is a member of several conference and industry committees. Chris regularly presents research innovations and teaches workshops on R, conjoint analysis, strategic modeling, and other analytics topics.

Elea McDonnell Feit is an Assistant Professor of Marketing at Drexel University and a Senior Fellow of Marketing at The Wharton School. She enjoys making quantitative methods accessible to a broad audience and teaches workshops and courses on advertising measurement, marketing experiments, marketing analytics in R, discrete choice modeling and hierarchical Bayes methods. She is an author of Chapman & Feit, R for Marketing Research and Analytics (Springer, 2015).

作者簡介(中文翻譯)

Jason Schwarz 博士 是 Google 的量化研究員，曾任系統神經生物學家。他的研究領域包括感知、注意力、動機、行為模式形成以及數據可視化，這些他在 Google 進行大規模研究。在加入 Google 之前，他曾在一家初創公司擔任數據科學家，負責分析並開發和部署基於 Python 的生產機器學習模型。

Chris Chapman 博士 是 Google 的量化研究員，也是 Chapman & Feit 的著作《R for Marketing Research and Analytics》（Springer, 2015）的作者。在更廣泛的行業中，他曾擔任美國市場營銷協會（American Marketing Association）實務委員會的主席，並在 2012 年和 2017 年主持 AMA 先進研究技術論壇，還是多個會議和行業委員會的成員。Chris 定期展示研究創新，並教授有關 R、聯合分析、策略建模及其他分析主題的工作坊。

Elea McDonnell Feit 是德雷克塞爾大學的市場營銷助理教授，也是沃頓商學院的市場營銷高級研究員。她喜歡讓量化方法對廣泛的受眾變得可及，並教授有關廣告測量、市場實驗、R 中的市場分析、離散選擇建模和層級貝葉斯方法的工作坊和課程。她是 Chapman & Feit 的著作《R for Marketing Research and Analytics》（Springer, 2015）的作者。

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