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|a 1449358659
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|a QA76.9.D343
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|a Schutt, Rachel
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|a Doing data science
|c Rachel Schutt, Cathy O'Neil
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| 260 |
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|a Sebastopol, CA
|b O'Reilly Media
|c 2014
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| 300 |
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|a 1 volume
|b illustrations
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|a A Data Scientist's Role in This Process -- Thought Experiment: How Would You Simulate Chaos? -- Case Study: RealDirect -- How Does RealDirect Make Money? -- Exercise: RealDirect Data Strategy -- Chapter3.Algorithms -- Machine Learning Algorithms -- Three Basic Algorithms -- Linear Regression -- k-Nearest Neighbors (k-NN) -- k-means -- Exercise: Basic Machine Learning Algorithms -- Solutions -- Summing It All Up -- Thought Experiment: Automated Statistician -- Chapter4.Spam Filters, Naive Bayes, and Wrangling -- Thought Experiment: Learning by Example
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|a The Current Landscape (with a Little History) -- Data Science Jobs -- A Data Science Profile -- Thought Experiment: Meta-Definition -- OK, So What Is a Data Scientist, Really? -- In Academia -- In Industry -- Chapter2.Statistical Inference, Exploratory Data Analysis, and the Data Science Process -- Statistical Thinking in the Age of Big Data -- Statistical Inference -- Populations and Samples -- Populations and Samples of Big Data -- Big Data Can Mean Big Assumptions -- Modeling -- Exploratory Data Analysis -- Philosophy of Exploratory Data Analysis -- Exercise: EDA -- The Data Science Process
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|a Why Won't Linear Regression Work for Filtering Spam? -- How About k-nearest Neighbors? -- Naive Bayes -- Bayes Law -- A Spam Filter for Individual Words -- A Spam Filter That Combines Words: Naive Bayes -- Fancy It Up: Laplace Smoothing -- Comparing Naive Bayes to k-NN -- Sample Code in bash -- Scraping the Web: APIs and Other Tools -- Jake's Exercise: Naive Bayes for Article Classification -- Sample R Code for Dealing with the NYT API -- Chapter5.Logistic Regression -- Thought Experiments -- Classifiers -- Runtime -- You -- Interpretability -- Scalability -- M6D Logistic Regression Case Study
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|a Copyright -- Table of Contents -- Preface -- Motivation -- Origins of the Class -- Origins of the Book -- What to Expect from This Book -- How This Book Is Organized -- How to Read This Book -- How Code Is Used in This Book -- Who This Book Is For -- Prerequisites -- Supplemental Reading -- About the Contributors -- Conventions Used in This Book -- Using Code Examples -- Safari® Books Online -- How to Contact Us -- Acknowledgments -- Chapter1.Introduction: What Is Data Science? -- Big Data and Data Science Hype -- Getting Past the Hype -- Why Now? -- Datafication
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|a Data mining / fast
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|a Data Mining
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|a Big data / fast
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|a Information science / http://id.loc.gov/authorities/subjects/sh85066150
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|a Big data / http://id.loc.gov/authorities/subjects/sh2012003227
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|a Données volumineuses
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|a Visualisation de l'information
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|a Data structures (Computer science) / http://id.loc.gov/authorities/subjects/sh85035862
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|a Data structures (Computer science) / fast
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|a Information science / fast
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|a Sciences de l'information
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|a Data mining / http://id.loc.gov/authorities/subjects/sh97002073
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|a Structures de données (Informatique)
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|a Information visualization / http://id.loc.gov/authorities/subjects/sh2002000243
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|a Exploration de données (Informatique)
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|b OREILLY
|a O'Reilly
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|z 9781449358655
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|x Verlag
|3 Volltext
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|a 006.3
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|a 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. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O'Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course
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