Doing Data Science: Straight Talk from the Frontline

Doing Data Science: Straight Talk from the Frontline
by Cathy O’Neil and Rachel Schutt
Print Length 页数: 408 pages
Publisher finelybook 出版社: O’Reilly Media; 1 edition (3 Nov. 2013)
Language 语言: English
ISBN-10: 1449358659
ISBN-13: 9781449358655
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.
现在人们意识到数据可以在选举或商业模式中发挥作用，数据科学作为一种职业正在增长。但是，如何开始在广泛的跨学科领域开展工作，如此混乱？基于哥伦比亚大学数据科学导论课的这本有见地的书，告诉你你需要知道什么。
在这些长篇大论中，Google，微软和eBay等公司的数据科学家通过介绍案例研究及其使用的代码来共享新的算法，方法和模型。如果您熟悉线性代数，概率和统计学知识，并且具有编程经验，这本书是数据科学的理想介绍。
主题包括:
统计推论，探索性数据分析和数据科学过程
算法
垃圾邮件过滤器，朴素贝叶斯和数据争吵
逻辑回归
财务模型
推荐引擎和因果关系
数据可视化
社交网络和数据新闻
数据工程，MapReduce，Pregel和Hadoop
数据科学是新闻集团数据科学高级副总裁Rachel Schutt和数据科学顾问Cathy O’Neil之间的合作，他是Johnson研究实验室的高级数据科学家，他参加了这个课程。

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