Doing Data Science

这不是一本教你写代码的手册，而是一份关于数据科学家如何工作的田野调查。全书由哥伦比亚大学的课程整理而成，每一章都请了一位来自Google、微软、eBay等公司的一线实践者现身说法，用真实案例和实战经验，拆解数据科学背后那些教科书不会明说的事：如何提出好问题、如何搭建数据管道、如何把发现讲成故事、如何为模型对他人生活造成的影响负责。读者最看重的，正是这种跨领域、全景式的'查漏补缺'——它不追求深度，却以提纲挈领的方式，让你快速看清这门学科的全貌与边界。它适合想初步了解数据科学的人，也适合已入门、希望补上工程、伦理与商业视角短板的人。

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 Research Scientist at Johnson Research Labs, and most recently was a Senior Statistician at Google Research in the New York office. She is also an adjunct assistant professor in the Department of Statistics at Columbia University where she taught Introduction to Data Science. 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. Rachel enjoys designing and creating complex, thought-provoking situations for other people. She won the Howard Levene Outstanding Teaching Award at Columbia and also taught probability and statistics at Cooper Union, and remedial math as a high school teacher in San Jose, CA. She was a mathematics curriculum expert for the Princeton Review, and won a game design award for best family game at the Come Out and Play Festival in New York.