没有合适的资源?快使用搜索试试~ 我知道了~
首页Art of Data Science
Data analysis is hard, and part of the problem is that few people can explain how to do it. It’s not that there aren’t any people doing data analysis on a regular basis. It’s that the people who are really good at it have yet to enlighten us about the thought process that goes on in their heads.
资源详情
资源评论
资源推荐


The Art of Data Science
A Guide for Anyone Who Works with Data
Roger D. Peng and Elizabeth Matsui
This book is for sale at
http://leanpub.com/artofdatascience
This version was published on 2017-01-13
This is a Leanpub book. Leanpub empowers authors and
publishers with the Lean Publishing process. Lean
Publishing is the act of publishing an in-progress ebook
using lightweight tools and many iterations to get reader
feedback, pivot until you have the right book and build
traction once you do.
© 2015 - 2017 Skybrude Consulting, LLC

Special thanks to Maggie Matsui, who created all of the artwork
for this book.

Contents
1. Data Analysis as Art . . . . . . . . . . . . . . . . . 1
2. Epicycles of Analysis . . . . . . . . . . . . . . . . . 4
2.1 Setting the Scene . . . . . . . . . . . . . . . . 5
2.2 Epicycle of Analysis . . . . . . . . . . . . . . 6
2.3 Setting Expectations . . . . . . . . . . . . . . 8
2.4 Collecting Information . . . . . . . . . . . . 9
2.5 Comparing Expectations to Data . . . . . . 10
2.6 Applying the Epicycle of Analysis Process . 11
3. Stating and Refining the Question . . . . . . . . 16
3.1 Types of Questions . . . . . . . . . . . . . . . 16
3.2 Applying the Epicycle to Stating and Refin-
ing Your Question . . . . . . . . . . . . . . . 20
3.3 Characteristics of a Good Question . . . . . 20
3.4 Translating a Question into a Data Problem 23
3.5 Case Study . . . . . . . . . . . . . . . . . . . . 26
3.6 Concluding Thoughts . . . . . . . . . . . . . 30
4. Exploratory Data Analysis . . . . . . . . . . . . . 31
4.1 Exploratory Data Analysis Checklist: A Case
Study . . . . . . . . . . . . . . . . . . . . . . . 33
4.2 Formulate your question . . . . . . . . . . . 33
4.3 Read in your data . . . . . . . . . . . . . . . . 35
4.4 Check the Packaging . . . . . . . . . . . . . . 36
4.5 Look at the Top and the Bottom of your Data 39
剩余161页未读,继续阅读
















安全验证
文档复制为VIP权益,开通VIP直接复制

评论0