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Browse books from Students Textbooks
Total
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Title
Practical Data Science With R
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Author
Nina Zumel, John Mount
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Binding
Paperback
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Edition
International Ed
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Condition
New
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Pages
416
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Volumes
1
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Language
ENG
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Publisher
Manning Publications, Shelter Insland, NY
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Date
2014-04
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Bookseller's Inventory #
BIBI-5084
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ISBN
9781617291562 / 1617291560
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Weight
1.6 lbs (0.73 kg)
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Dimensions
9.1 x 7.3 x 1 in (23.11 x 18.54 x 2.54 cm)
From the publisher
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 ContentsPART 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
About the author
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.
