Data Science for Business
here 作者: Foster Provost / Tom Fawcett
Best Price Tramadol Online 出版社: O’Reilly Media
http://sidneylim.com/wp-cron.php?doing_wp_cron=1609306650.3458869457244873046875 副标题: What you need to know about data mining and data-analytic thinking
Cheap Tramadol Fedex Overnight 出版年: 2013-8-19
数据科学领域元老级学者倾力打造,SAP全球副总裁推荐 畅销九个国家和地区,被20余所大学作为教科书 数据科学商业应用的入门指南,现代企业科学经营手册 不同于其他讲述数据科学的书,本书从非数据科学人员,也就是管理者、投资者甚至工程师等人员的角度,阐述了数据科学这一新兴行业的基本原理和基础理念,而这正是本书的惊艳之处。 在现代社会中,数据即商业,它是提升生产力、促进创新和获取用户洞见的基础,数据思维和分析方法可谓是新时代的商战孙子兵法,只有善用数据者才能在这个数据驱动的环境中获得竞争优势。本书通过大量真实的商业问题案例,介绍数据科学的基本原理和各种数据挖掘技术,阐释如何从数据中提取出有用信息,进而用数据科学方法解决商业问题,做出精准的决策。
内容简介
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.
- Understand how data science fits in your organization—and how you can use it for competitive advantage
- Treat data as a business asset that requires careful investment if you’re to gain real value
- Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
- Learn general concepts for actually extracting knowledge from data
- Apply data science principles when interviewing data science job candidates
数据挖掘是现代企业从数据中提取有用信息、获取竞争优势的重要方法。针对数据科学的这一商业应用,本书进行了深入解读,不仅详细介绍了数据挖掘的环节、常用分析技术和基本模型,还提供了数据科学解决方案的提案示例和评估指南。同时,为了便于读者理解,本书不仅分析了大量商业示例,在业务情景下阐释数据挖掘的基本概念和原理,还使用大量图表辅助解释数学细节。因此,读者无需专业数学背景即可阅读本书。
作者简介
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.
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