Practical facts technological know-how with R lives as much as its identify. It explains simple ideas with out the theoretical mumbo-jumbo and jumps correct to the genuine use circumstances you are going to face as you gather, curate, and study the information an important to the good fortune of your enterprise. you will observe the R programming language and statistical research options to scrupulously defined examples established in advertising, company intelligence, and choice support.
Purchase of the print booklet incorporates a unfastened booklet in PDF, Kindle, and ePub codecs from Manning Publications.
About the Book
Business analysts and builders are more and more accumulating, curating, examining, and reporting on an important enterprise information. The R language and its linked instruments supply a simple strategy to take on day by day facts technology projects with no lot of educational thought or complex mathematics.
Practical info technology with R indicates you ways to use the R programming language and priceless statistical strategies to daily enterprise events. utilizing examples from advertising, company intelligence, and determination aid, it indicates you the way to layout experiments (such as A/B tests), construct predictive versions, and current effects to audiences of all levels.
This ebook is available to readers with out a heritage in facts technological know-how. a few familiarity with simple facts, R, or one other scripting language is assumed.
- Data technology for the enterprise professional
- Statistical research utilizing the R language
- Project lifecycle, from making plans to delivery
- Numerous immediately generic use cases
- Keys to potent information presentations
About the Authors
Nina Zumel and John Mount are cofounders of a San Francisco-based facts technological know-how consulting company. either carry PhDs from Carnegie Mellon and weblog on records, chance, and laptop technological know-how at win-vector.com.
Table of Contents
PART 1 advent TO info SCIENCE
- The info technological know-how process
- Loading information into R
- Exploring data
- Managing data
PART 2 MODELING METHODS
- Choosing and comparing models
- Memorization methods
- Linear and logistic regression
- Unsupervised methods
- Exploring complicated methods
PART three offering RESULTS
- Documentation and deployment
- Producing powerful presentations