出版社: O'Reilly Media
副标题: Straight Talk from the Frontline
出版年: 2013-10-30
页数: 352
定价: USD 44.99
装帧: Paperback
ISBN: 9781449358655
内容简介 · · · · · ·
Now that answering complex and compelling questions with data can make the difference in an election or a business model, data science is an attractive discipline. But how can you learn this wide-ranging, interdisciplinary field? With this book, you’ll get material from Columbia University’s "Introduction to Data Science" class in an easy-to-follow format.
Each chapter-long lec...
Now that answering complex and compelling questions with data can make the difference in an election or a business model, data science is an attractive discipline. But how can you learn this wide-ranging, interdisciplinary field? With this book, you’ll get material from Columbia University’s "Introduction to Data Science" class in an easy-to-follow format.
Each chapter-long lecture features a guest data scientist from a prominent company such as Google, Microsoft, or eBay teaching new algorithms, methods, or models by sharing case studies and actual code they use. You’ll learn what’s involved in the lives of data scientists and be able to use the techniques they present.
Guest lectures focus on topics such as:
Machine learning and data mining algorithms
Statistical models and methods
Prediction vs. description
Exploratory data analysis
Communication and visualization
Data processing
Big data
Programming
Ethics
Asking good questions
If you’re familiar with linear algebra, probability and statistics, and have some programming experience, this book will get you started with data science.
Doing Data Science is collaboration between course instructor Rachel Schutt (also employed by Google) and data science consultant Cathy O’Neil (former quantitative analyst for D.E. Shaw) who attended and blogged about the course.
作者简介 · · · · · ·
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,...
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.
原文摘录 · · · · · ·
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Doing Data Science的书评 · · · · · · ( 全部 4 条 )
Doing Data Science
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论坛 · · · · · ·
话说这本书就是那个线上课程的纸质版本 | 来自panco | 2 回应 | 2013-11-05 23:54:06 |
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人民邮电出版社 (2015)8.0分 199人读过
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0 有用 Selaginella 2013-11-06 14:13:28
基本翻了一遍。想在这么薄一本书里把doing data science的思想、方法和主要应用方面说清楚很难,只能做到提纲挈领,适合想初步了解数据分析的人。着重看了生物统计的几章,点出实验设计和难以重复的弊病,以及极为中肯的3条核心建议(305页)。但是想知道具体每个领域怎么操作实在不够,需要看更针对的书籍。最后实在感谢船长老师的书!
1 有用 大啸 2015-08-07 18:45:30
什么都有 扫一遍可以查漏补缺 我的问题是缺乏代码和可视化 plus #治愈失眠无效#
0 有用 wacow 2013-11-20 23:43:15
看这种书主要不是看算法吧,主要看看一个“流程性”的东西,拿到数据,怎么explore,怎么telling story,试model之类的。 里面有些和不同公司访谈性的东西还比较有趣。
0 有用 碳基体 2015-08-01 11:11:09
使用R来学习数据科学,有算法,有实例,不错
1 有用 阿道克 2014-12-23 23:16:45
一本400页的书,讲明白data science,勉为其难啊。不过总得有人给数据科学作为一个完整的主题开个著书立说的头不是。
0 有用 躺平的大貓🐈 2023-03-24 15:02:27 中国台湾
Practical solutions
0 有用 小盼 2020-10-24 19:29:07
大概知道数据科学是啥了
0 有用 安吉 2020-07-02 20:17:00
大学的时候请过Cathy ONeil给我们讲课
0 有用 辣条 2019-09-27 22:40:12
很好的入门书
0 有用 F | Wagon 2019-07-10 19:32:51
这本够科普扫盲了。