Data Science for Business

Foster Provost

出版时间

2013-08-16

ISBN

9781449361327

评分

★★★★★
书籍介绍

Review

"A must-read resource for anyone who is serious about embracing the opportunity of big data."

-- Craig Vaughan

Global Vice President at SAP

"This book goes beyond data analytics 101. It's the essential guide for those of us (all of us?) whose businesses are built on the ubiquity of data opportunities and the new mandate for data-driven decision-making."

--Tom Phillips

CEO of Media6Degrees and Former Head of Google Search and Analytics

"Data is the foundation of new waves of productivity growth, innovation, and richer customer insight. Only recently viewed broadly as a source of competitive advantage, dealing well with data is rapidly becoming table stakes to stay in the game. The authors' deep applied experience makes this a must read--a window into your competitor's strategy."

-- Alan Murray

Serial Entrepreneur; Partner at Coriolis Ventures

"This timely book says out loud what has finally become apparent: in the modern world, Data is Business, and you can no longer think business without thinking data. Read this book and you will understand the Science behind thinking data."

-- Ron Bekkerman

Chief Data Officer at Carmel Ventures

"A great book for business managers who lead or interact with data scientists, who wish to better understand the principles and algorithms available without the technical details of single-disciplinary books."

-- Ronny Kohavi

Partner Architect at Microsoft Online Services Division

About the Author

Foster Provost is Professor and NEC Faculty Fellow at the NYU Stern School of Business where he teaches in the MBA, Business Analytics, and Data Science programs. His award-winning research is read and cited broadly. Prof. Provost has co-founded several successful companies focusing on data science for marketing.

Tom Fawcett holds a Ph.D. in machine learning and has worked in industry R&D for more than two decades for companies such as GTE Laboratories, NYNEX/Verizon Labs, and HP Labs. His published work has become standard reading in data science.

AI导读
核心看点
  • 聚焦数据科学思维而非代码实现
  • 阐述数据如何转化为商业战略资产
  • 讲解预测建模与决策分析的核心逻辑
适合谁读
  • 希望用数据驱动决策的商业管理者
  • 缺乏技术背景但需理解数据科学的人
  • 寻求技术与业务沟通桥梁的数据从业者
读前提醒
  • 本书不涉及深层数学推导与代码细节
  • 重点理解分析思维框架而非算法公式
  • 适合建立宏观认知,不适合深入技术实战
读者共识
  • 是连接数据技术与商业应用的优秀桥梁
  • 内容偏基础,适合入门或科普阅读
  • 有助于提升与非技术人员沟通的能力

本导读基于书籍简介、目录、原文摘录、短评和书评生成,不等同于全文精读。

精彩摘录
  • "data, and the capability to extract useful knowledge from data, should be regarded as key strategic assets"
  • "Understanding the whole process helps to structure data mining projects, so they are closer to systematic analyses rather than heroic endeavors driven by chance and individual acumen."
  • "Supervised tasks require different techniques than unsupervised tasks do, and the results often are much more useful."
目录
Preface
1. Intorduction: Data-Analytic Thinking
2. Business Problems and Data Science Solutions
3. Introduction to Predictive Modeling: From Correlation to Supervised Segmentation
4. Fitting a Model to Data

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用户评论
满满五颗星
去年粗略翻过一遍,无干货,也无甚湿货。没必要看的书。除非你想学一堆 biz 词汇去唬人。
Incredible book with practical example and strategical view. Simply explained how to leverage the real "Big Data" into data-driven business and being real value added.
教科书
学习如何把技术与business联系起来的不二法门。
Data mining is not just a catchy phrase. 来自门外汉的一点小感叹。
给非数据分析/挖掘人员看的科普书,当然明显是给管理层看的。
纪念此门水课
入门还行
如果你有数据科学的技术背景,想多了解在商业应用层面的细节,这本书应该满足不了你。这本书在技术和商业两侧都浅尝辄止,每当我想知道更多商业案例的细节时,作者就转向了技术科普。但是科普部分为了避免使用技术语言,反而又使得表述变得更加抽象。
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