Link to Content:

Created/Published/Taught by:

Nina Zumel

John Mount

Manning Publications

Content Found Via:

@WinVectorLLC

Free? No

Cost Range:

$40.42 - $49.99

Tags: data management / data science / exploratory data analysis / linear regression / logistic regression / predictive modeling / R / unsupervised learning

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Note: You could win this book in the giveaway contest going on from now until 12/12/2015! See blog post for details: 3 Ways to Win

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**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

- The data science process
- Loading data into R
- Exploring data
- Managing data

PART 2 MODELING METHODS

- Choosing and evaluating models
- Memorization methods
- Linear and logistic regression
- Unsupervised methods
- Exploring advanced methods

PART 3 DELIVERING RESULTS

- Documentation and deployment
- Producing effective presentations

Recommended Prerequisites: Some familiarity with basic statistics, R, or another scripting language is assumed.

Go to Content: Practical Data Science with R

## By cuenta4384 January 27, 2016 - 3:23 pm

I love the enhancement of this book, because no just show you the theory but also give you practice experience in the world of Data Science. After all that it’s primordial to guarantee the learning by doing some things.

Advanced Beginner- familiar with data science / new to this topic / some prerequisitesAdvanced Beginner - was new to this topic / had some prerequisites

(4) A Lot

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