Doing Data Science读书介绍
| 类别 | 页数 | 译者 | 网友评分 | 年代 | 出版社 |
|---|---|---|---|---|---|
| 书籍 | 352页 | 8.2 | 2020 | O'Reilly Media |
| 定价 | 出版日期 | 最近访问 | 访问指数 |
|---|---|---|---|
| USD 44.99 | 2020-02-20 … | 2020-06-03 … | 34 |
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,...
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