Data Mining for Business Analytics: Concepts, Techniques and Applications in Python (Hardcover)
暫譯: 商業分析的資料探勘：Python中的概念、技術與應用 (精裝版)

Shmueli, Galit, Bruce, Peter C., Gedeck, Peter

Data Mining for Business Analytics: Concepts, Techniques and Applications in Python (Hardcover)

商品描述

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration

Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities.

This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:

  • A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process
  • A new section on ethical issues in data mining
  • Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students
  • More than a dozen case studies demonstrating applications for the data mining techniques described
  • End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
  • A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

"This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject."

--Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

商品描述(中文翻譯)

《商業分析的資料探勘：概念、技術與在 Python 中的應用》提供了一種應用的資料探勘概念和方法的方式，並使用 Python 軟體進行說明。

讀者將學習如何在 Python（免費且開源的軟體）中實現各種流行的資料探勘演算法，以解決商業問題和機會。

這是這本成功教材的第六版，也是第一個使用 Python 的版本。它涵蓋了預測、分類、視覺化、降維、推薦系統、聚類、文本探勘和網路分析的統計和機器學習演算法。它還包括：

- 一位新的共同作者，Peter Gedeck，他在使用 Python 教授商業分析課程方面擁有經驗，並在將機器學習方法應用於藥物發現過程方面具有專業知識。
- 一個關於資料探勘倫理問題的新章節。
- 根據教授 MBA、學士、文憑和高階課程的教師及其學生的反饋進行的更新和新材料。
- 超過十幾個案例研究，展示所描述的資料探勘技術的應用。
- 章末練習，幫助讀者評估和擴展對所呈現材料的理解和能力。
- 一個伴隨網站，提供超過二十個數據集，以及包括練習解答、PowerPoint 幻燈片和案例解決方案的教學材料。

《商業分析的資料探勘：概念、技術與在 Python 中的應用》是研究生和高年級本科生資料探勘、預測分析和商業分析課程的理想教科書。這一新版也是分析師、研究人員和從事商業、金融、行銷、計算機科學和資訊技術領域的定量方法的實務工作者的優秀參考書。

「這本書是我見過的對商業分析方法最全面的回顧，涵蓋了從經典方法如線性和邏輯回歸，到現代方法如神經網絡、集成學習和提升，甚至還包括許多更具商業特定程序的社交網絡分析和文本探勘。如果不是聖經，至少也是這個主題的權威手冊。」

--Gareth M. James，南加州大學，與 Witten、Hastie 和 Tibshirani 共同撰寫暢銷書《統計學習導論：R 的應用》。

作者簡介

GALIT SHMUELI, PHD, is Distinguished Professor at National Tsing Hua University's Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 100 publications including books.

PETER C. BRUCE is President and Founder of the Institute for Statistics Education at Statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective (Wiley) and co-author of Practical Statistics for Data Scientists: 50 Essential Concepts (O'Reilly).

PETER GEDECK, PHD, is a Senior Data Scientist at Collaborative Drug Discovery, where he helps develop cloud-based software to manage the huge amount of data involved in the drug discovery process. He also teaches data mining at Statistics.com.

NITIN R. PATEL, PhD, is cofounder and board member of Cytel Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years.

作者簡介(中文翻譯)

加利特·施穆埃利 (GALIT SHMUELI), PhD 是國立清華大學服務科學研究所的特聘教授。自2004年以來，她在馬里蘭大學、Statistics.com、印度商學院和國立清華大學教授資料探勘課程。施穆埃利教授以其在商業分析方面的研究和教學而聞名，專注於資訊系統和醫療保健中的統計及資料探勘方法。她已發表超過100篇出版物，包括書籍。

彼得·C·布魯斯 (PETER C. BRUCE) 是Statistics.com統計教育研究所的總裁和創始人。他撰寫了多篇期刊文章，並開發了Resampling Stats軟體。他是《入門統計與分析：重抽樣的視角》（Introductory Statistics and Analytics: A Resampling Perspective，Wiley）的作者，並且是《資料科學家的實用統計：50個基本概念》（Practical Statistics for Data Scientists: 50 Essential Concepts，O'Reilly）的共同作者。

彼得·蓋德克 (PETER GEDECK), PhD 是Collaborative Drug Discovery的高級資料科學家，協助開發雲端軟體以管理藥物發現過程中涉及的大量數據。他也在Statistics.com教授資料探勘課程。

尼廷·R·帕特爾 (NITIN R. PATEL), PhD 是位於麻薩諸塞州劍橋的Cytel Inc.的共同創辦人及董事會成員。作為美國統計協會的會士，帕特爾博士曾擔任麻省理工學院和哈佛大學的訪問教授。他是印度計算機學會的會士，並在印度管理學院艾哈邁達巴德任教15年。

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