Practical Data Science With R - Brossura

Zumel, Nina; Mount, John

Brossura

ISBN 10: 1617295876 ISBN 13: 9781617295874

Casa editrice: Manning Pubns Co, 2019

Riassunto

Summary

Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever expanding field of data science. You'll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Foreword by Jeremy Howard and Rachel Thomas

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

About the Technology

Evidence-based decisions are crucial to success. Applying the right data analysis techniques to your carefully curated business data helps you make accurate predictions, identify trends, and spot trouble in advance. The R data analysis platform provides the tools you need to tackle day-to-day data analysis and machine learning tasks efficiently and effectively.

About the Book

Practical Data Science with R, Second Edition is a task-based tutorial that leads readers through dozens of useful, data analysis practices using the R language. By concentrating on the most important tasks you'll face on the job, this friendly guide is comfortable both for business analysts and data scientists. Because data is only useful if it can be understood, you'll also find fantastic tips for organizing and presenting data in tables, as well as snappy visualizations.

What's inside

• Statistical analysis for business pros
• Effective data presentation
• The most useful R tools
• Interpreting complicated predictive models

About the Reader

You'll need to be comfortable with basic statistics and have an introductory knowledge of R or another high-level programming language.

About the Author

Nina Zumel and John Mount founded a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon University and blog on statistics, probability, and computer science.

Table of Contents

PART 1 - INTRODUCTION TO DATA SCIENCE

1. The data science process
2. Starting with R and data
3. Exploring data
4. Managing data
5. Data engineering and data shaping

PART 2 - MODELING METHODS

6. Choosing and evaluating models
7. Linear and logistic regression
8. Advanced data preparation
9. Unsupervised methods
10. Exploring advanced methods

PART 3 - WORKING IN THE REAL WORLD

11. Documentation and deployment
12. Producing effective presentations

Su questo libro

Informazioni sugli autori

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.

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.

Dettagli dell?edizione

• Editore: Manning Pubns Co
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 1617295876
• ISBN 13: 9781617295874
• Rilegatura: Copertina flessibile
• Numero edizione: 2
• Numero di pagine: 536
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile