Data Science for Business英文原版PDF 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.

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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 standar

Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets. 通过数据科学学习数据科学！ 数据科学使用Python和R将使您进入世界上两个最广泛的数据科学开源平台：Python和R. 数据科学很热门。 Bloomberg称数据科学家是“美国最热门的工作。”Python和R是世界上最畅销的两个开源数据科学工具。在使用Python和R的数据科学中，您将逐步学习如何使用最先进的技术为现实世界

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[奥莱理] Doing Data Science (英文版) [奥莱理] Doing Data Science Straight Talk from the Frontline (E-Book) ☆ 图书概要：☆ Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. ☆ 出版信息：☆ [作者信息] Rachel Schutt , Cathy O'Neil [出版机构] 奥莱理 [出版日期] 2013年10月31日 [图书页数] 406页 [图书语言] 英语 [图书格式] PDF 格式

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一种新科学A new kind of science便捷版// * [导语]// * [第一章 一种新科学的建立基础]// * [第二章 关键的实验]// * [第三章 简单程序的世界]// * [第四章 基于数字的系统]// * [第五章 二维及之上]// * [导语]/ * [元胞自动机]/ * [图灵机]/ * [替换系统与分形]/ * [网络系统]/ * [第六章 由随机开始]// * [第七章 程序与自然中的机制]// * [第八章 日常系统的启示]// * [第九章 基础物理]// * [第十章 感知和分析的过程]// * [第十一章 计算等价性原理]///