Practical.Data.Science.with.R.2nd.Edition
Practical.Data.Science.with.R.2nd.Edition 豆瓣
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ISBN:9781617295874
作者: Nina Zumel / John Mount
出版社:Manning Publications
出版时间:2019年
装帧:Paperback
定价:USD 49.99
页数:448
简介

Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever expanding field of data science. You'll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Foreword by Jeremy Howard and Rachel Thomas
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
Evidence-based decisions are crucial to success. Applying the right data analysis techniques to your carefully curated business data helps you make accurate predictions, identify trends, and spot trouble in advance. The R data analysis platform provides the tools you need to tackle day-to-day data analysis and machine learning tasks efficiently and effectively.
About the Book
Practical Data Science with R, Second Edition is a task-based tutorial that leads readers through dozens of useful, data analysis practices using the R language. By concentrating on the most important tasks you'll face on the job, this friendly guide is comfortable both for business analysts and data scientists. Because data is only useful if it can be understood, you'll also find fantastic tips for organizing and presenting data in tables, as well as snappy visualizations.
What's inside
Statistical analysis for business pros
Effective data presentation
The most useful R tools
Interpreting complicated predictive models
About the Reader
You'll need to be comfortable with basic statistics and have an introductory knowledge of R or another high-level programming language.

目录

Table of Contents
PART 1 - INTRODUCTION TO DATA SCIENCE
The data science process
Starting with R and data
Exploring data
Managing data
Data engineering and data shaping
PART 2 - MODELING METHODS
Choosing and evaluating models
Linear and logistic regression
Advanced data preparation
Unsupervised methods
Exploring advanced methods
PART 3 - WORKING IN THE REAL WORLD
Documentation and deployment
Producing effective presentations

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