Link to Content:

Art of Data Science on LeanPub

Created/Published/Taught by:

Roger D. Peng

Elizabeth Matsui

Content Found Via:

Roger Peng on LinkPub

Free? Partially: Some Free Content, Some Paid

Cost Range:

$0.00 - $15.00

Tags: data analysis / data modeling / data science (overview) / exploratory data analysis / inference / prediction

Difficulty Rating:

from LinkPub:

“A Guide for Anyone Who Works with Data

This book describes, simply and in general terms, the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

About the Book

Data analysis is a difficult process largely because few people can describe exactly how to do it. It’s not that there aren’t any people doing data analysis on a regular basis. It’s that the process by which we state a question, explore data, conduct formal modeling, interpret results, and communicate findings, is a difficult process to generalize and abstract.

Fundamentally, data analysis is an art. It is not yet something that we can teach to a computer. Data analysts have many tools at their disposal, from linear regression to classification trees to random forests, and these tools have all been carefully implemented on computers. But ultimately, it takes a data analyst—a person—to find a way to assemble all of the tools and apply them to data to answer a question of interest to people.

This book writes down the process of data analysis with a minimum of technical detail. What we describe is not a specific “formula” for data analysis, but rather is a general process that can be applied in a variety of situations. Through our extensive experience both managing data analysts and conducting our own data analyses, we have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of our experience in a format that is applicable to both practitioners and managers in data science.

We sincerely believe that this book should be useful for anyone who has to work with data.”

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