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Practical Data Science with R
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(Buch) |
Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!
| Lieferstatus: |
 Vorankündigung |
| Veröffentlichung: |
ANGEKÜNDIGT
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| Genre: |
EDV / Informatik |
| ISBN: |
9781617291562 |
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EAN-Code:
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9781617291562 |
| Verlag: |
Pearson Academic |
| Einband: |
Kartoniert |
| Sprache: |
English
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| Serie: |
Manning |
| Dimensionen: |
H 233 mm / B 184 mm / D 25 mm |
| Gewicht: |
706 gr |
| Bewertung: |
Keine Bewertung vor Veröffentlichung möglich.
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| Inhalt: |
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
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