|Practical Data Science with R
Nina Zumel and John Mount
MEAP Began: May 2013
Softbound print: May 2014 (est.) | 450 pages
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Table of Contents, MEAP Chapters & Resources
|Table of Contents||Resources|
PART 1: INTRODUCTION TO DATA SCIENCE
1 The Data Science Process - FREE
2 Loading Data into R - AVAILABLE
3 Exploring Data - AVAILABLE
4 Managing Data - AVAILABLE
PART 2:MODELING METHODS
5 Choosing and Evaluating Models - AVAILABLE
6 Using Memorization Methods - AVAILABLE
7 Using Linear and Logistic Regression - AVAILABLE
8 Using Unsupervised Methods - AVAILABLE
9 Exploring Advanced Methods
PART 3: DELIVERING RESULTS
10 Deployment and Documentation
12 Conclusion, What to Take Away
A Working With R and Other Tools
B Important Statistical Concepts
Simply put, data science is the discipline of extracting meaning from data. More and more business analysts are called to work as data scientists. 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 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. Using these examples, you'll learn how to create instrumentation, to design experiments such as A/B tests, and to accurately present data to audiences of all levels.
- Data science for the motivated business professional
- Applying the R programming language for statistical analysis
- Right-sized examples that match your real-world concerns
- Demonstrations of statistical ideas you need to know
- All aspects of the project lifecycle, from planning to final delivery
- Learn from well chosen examples
Written for the business analyst, technical consultant or technical director, no formal statistics or mathematics background is required. Readers should be comfortable with quantitative thinking plus light scripting or programming. Some familiarity with R is a plus.
About the Authors
Nina Zumel and John Mount are co-founders of Win-Vector, a data science consulting firm in San Francisco. Nina 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. John 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. Both contribute to the Win-Vector Blog, which covers topics in statistics, probability, computer science, mathematics and optimization.
About the Early Access Version
This Early Access version of Practical Data Science with R enables you to receive new chapters as they are being written. You can also interact with the authors to ask questions, provide feedback and errata, and help shape the final manuscript on the Author Online forum.
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