Practical Data Science with R (Paperback)
Nina Zumel, John Mount
- 出版商: Manning
- 出版日期: 2014-04-22
- 定價: $1,650
- 售價: 6.0 折 $990
- 語言: 英文
- 頁數: 389
- 裝訂: Paperback
- ISBN: 1617291560
- ISBN-13: 9781617291562
-
相關分類:
R 語言、資料科學
-
相關翻譯:
數據科學:理論、方法與R語言實踐 (簡中版)
-
其他版本:
Practical Data Science with R 2nd Edition
立即出貨(限量) (庫存=3)
此書已有新版 ↗️買這商品的人也買了...
-
$550$468 -
$680$578 -
$650$514 -
$360$281 -
$580$452 -
$1,520Big Data: Principles and best practices of scalable realtime data systems (Paperback)
-
$400$380 -
$480$408 -
$945R for Everyone: Advanced Analytics and Graphics (Paperback)
-
$480$408 -
$680$578 -
$550$468 -
$1,730$1,644 -
$580$458 -
$480$408 -
$2,100$1,995 -
$360$284 -
$780$663 -
$500$425 -
$540$459 -
$650$507 -
$290$261 -
$1,760$1,672 -
$1,600$1,520 -
$720$569
相關主題
商品描述
Summary
Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Book
Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics.
Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels.
This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed.
What's Inside
- Data science for the business professional
- Statistical analysis using the R language
- Project lifecycle, from planning to delivery
- Numerous instantly familiar use cases
- Keys to effective data presentations
About the Authors
Nina Zumel and John Mount are cofounders of a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com.
Table of Contents
PART 1 INTRODUCTION TO DATA SCIENCE
PART 2 MODELING METHODS
PART 3 DELIVERING RESULTS
- The data science process
- Loading data into R
- Exploring data
- Managing data
- Choosing and evaluating models
- Memorization methods
- Linear and logistic regression
- Unsupervised methods
- Exploring advanced methods
- Documentation and deployment
- Producing effective presentations