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Practical Data Science with R

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Practical Data Science with R Cover

 

Synopses & Reviews

Publisher Comments:

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
  5. PART 2 MODELING METHODS
  6. Choosing and evaluating models
  7. Memorization methods
  8. Linear and logistic regression
  9. Unsupervised methods
  10. Exploring advanced methods
  11. PART 3 DELIVERING RESULTS
  12. Documentation and deployment
  13. Producing effective presentations

Synopsis:

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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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.

Product Details

ISBN:
9781617291562
Author:
Zumel, Nina
Publisher:
Manning Publications
Author:
Mount, John
Subject:
Data processing
Subject:
R;SVM;analytics;association rules;classification;data mining;data science;decision trees;linear regression;logistic regression;machine learning;naive bayes;nearest neighbor;prediction;predictive analytics;predictive models;probability;regression;statistic
Subject:
R;SVM;analytics;association rules;classification;data mining;data science;decision trees;linear regression;logistic regression;machine learning;naive bayes;nearest neighbor;prediction;predictive analytics;predictive models;probability;regression;statistic
Subject:
R;SVM;analytics;association rules;classification;data mining;data science;decision trees;linear regression;logistic regression;machine learning;naive bayes;nearest neighbor;prediction;predictive analytics;predictive models;probability;regression;statistic
Subject:
R;SVM;analytics;association rules;classification;data mining;data science;decision trees;linear regression;logistic regression;machine learning;naive bayes;nearest neighbor;prediction;predictive analytics;predictive models;probability;regression;statistic
Subject:
R;SVM;analytics;association rules;classification;data mining;data science;decision trees;linear regression;logistic regression;machine learning;naive bayes;nearest neighbor;prediction;predictive analytics;predictive models;probability;regression;statistic
Subject:
R;SVM;analytics;association rules;classification;data mining;data science;decision trees;linear regression;logistic regression;machine learning;naive bayes;nearest neighbor
Subject:
;prediction;predictive analytics;predictive models;probability;regression;statistics
Subject:
R;SVM;analytics;association rules;classification;data mining;data science;decision trees;linear regression;logistic regression;machine learning;naive bayes;nearest neighbor;prediction;predictive analytics;predictive models;probability;regression;statistic
Edition Description:
Print PDF
Publication Date:
20140431
Binding:
TRADE PAPER
Language:
English
Pages:
416
Dimensions:
9.25 x 7.38 in

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Related Subjects

Computers and Internet » Artificial Intelligence » General
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Science and Mathematics » Mathematics » Software

Practical Data Science with R New Trade Paper
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$49.99 In Stock
Product details 416 pages Manning Publications - English 9781617291562 Reviews:
"Synopsis" by ,

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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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