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Informationen zum Autor Cathy O'Neil earned a Ph.D. in math from Harvard, was postdoc at the MIT math department, and a professor at Barnard College where she published a number of research papers in arithmetic algebraic geometry. She then chucked it and switched over to the private sector. She worked as a quant for the hedge fund D.E. Shaw in the middle of the credit crisis, and then for RiskMetrics, a risk software company that assesses risk for the holdings of hedge funds and banks. She is currently a data scientist on the New York start-up scene, writes a blog at mathbabe.org, and is involved with Occupy Wall Street. Rachel Schutt is a Senior Statistician at Google Research in the New York office and adjunct assistant professor at Columbia University. She earned a PhD from Columbia University in statistics, and masters degrees in mathematics and operations research from the Courant Institute and Stanford University, respectively. Her statistical research interests include modeling and analyzing social networks, epidemiology, hierarchical modeling and Bayesian statistics. Her education-related research interests include curriculum design. Klappentext 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. Zusammenfassung 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 tells you what you need to know. Inhaltsverzeichnis Dedication Preface Chapter 1: Introduction: What Is Data Science? Chapter 2: Statistical Inference, Exploratory Data Analysis, and the Data Science Process Chapter 3: Algorithms Chapter 4: Spam Filters, Naive Bayes, and Wrangling Chapter 5: Logistic Regression Chapter 6: Time Stamps and Financial Modeling Chapter 7: Extracting Meaning from Data Chapter 8: Recommendation Engines: Building a User-Facing Data Product at Scale Chapter 9: Data Visualization and Fraud Detection Chapter 10: Social Networks and Data Journalism Chapter 11: Causality Chapter 12: Epidemiology Chapter 13: Lessons Learned from Data Competitions: Data Leakage and Model Evaluation Chapter 14: Data Engineering: MapReduce, Pregel, and Hadoop Chapter 15: The Students Speak Chapter 16: Next-Generation Data Scientists, Hubris, and Ethics Index Colophon ...
