Practical Data Science with R

Nina Zumel

ISBN 10: 1617291560 / ISBN 13: 9781617291562

Verlag: Manning Publications, United States, New York, 2014

Beschreibung

DESCRIPTION Simply put, data science is the discipline of extracting meaning from data. While it can involve deep knowledge of statistics, mathematics, machine learning, and computer science, for most non-academics, data science looks like applying analysis techniques to answer key business questions. 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 faced while collecting, curating, and analyzing the data crucial to the success of businesses. Readers will apply the R programming language and statistical analysis techniques to carefully-explained examples based in marketing, business intelligence, and decision support, while learning how to create instrumentation, design experiments such as A/B tests, and accurately present data to audiences of all levels. RETAIL SELLING POINTS Demonstrations of need-to-know statistical ideas Covers all aspects of the project lifecycle Data science for the motivated business professional AUDIENCE Written for the business analyst, technical consultant or technical director no formal statistics or mathematics background is required. Readers should be comfortable with quantitative thinking plus light scripting or programming. Some familiarity with R is a plus. ABOUT THE TECHNOLOGY R is a programming language which is used for developing statistical software programs. Data Science is the process of collecting data and developing analysis techniques and software over that data to answer key business questions. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Bestandsnummer des Verkäufers GOR009212934

Bibliografische Details

• Titel: Practical Data Science with R
• Verlag: Manning Publications, United States, New York
• Erscheinungsjahr: 2014
• Sprache: Englisch
• ISBN-10: 1617291560
• ISBN-13: 9781617291562
• Einband: Paperback
• Zustand: Very Good
• Gewicht: 735 Gramm

Über diesen Titel

Inhaltsangabe

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

1. The data science process
2. Loading data into R
3. Exploring data
4. Managing data

PART 2 MODELING METHODS

5. Choosing and evaluating models
6. Memorization methods
7. Linear and logistic regression
8. Unsupervised methods
9. Exploring advanced methods

PART 3 DELIVERING RESULTS

10. Documentation and deployment
11. Producing effective presentations

Über die Autorin bzw. den Autor

Nina Zumel co-founded Win-Vector, a data science consulting firm in San Francisco. She holds a PH.D. in robotics from Carnegie Mellon and was a content developer for EMC's Data Science and Big Data Analytics Training Course. Nina also contributes to the Win-Vector Blog, which covers topics in statistics, probability, computer science, mathematics and optimization.

John Mount co-founded Win-Vector, a data science consulting firm in San Francisco. He has a Ph.D. in computer science from Carnegie Mellon and over 15 years of applied experience in biotech research, online advertising, price optimization and finance. He contributes to the Win-Vector Blog, which covers topics in statistics, probability, computer science, mathematics and optimization.