• Title Introduction to Data Science, with Introduction to R
• Author(s) Jeffrey Stanton
• Publisher: SAGE (October 6, 2017); eBook (Creative Commons, Syracuse University, 2013)
• License(s): CC BY-NC-SA 3.0
• Hardcover/Paperback 288 pages
• eBook PDF, ePub, Kindle, etc.
• Language: English
• ISBN-10/ASIN: 150637753X
• ISBN-13: 978-1506377537
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Book Description

This book provides non-technical readers with a gentle introduction to essential concepts and activities of data science. For more technical readers, the book provides explanations and code for a range of interesting applications using the open source R language for statistical computing and graphics.

It also addresses the various skills required, the key steps in the Data Science process, software technology related to the effective practice of Data Science, and the best rising academic programs for training in the field.

In this book, a series of data problems of increasing complexity is used to illustrate the skills and capabilities needed by data scientists. The open source data analysis program known as "R" and its graphical user interface companion "R-Studio" are used to work with real data examples to illustrate both the challenges of data science and some of the techniques used to address those challenges. To the greatest extent possible, real datasets reflecting important contemporary issues are used as the basis of the discussions.

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• Data Science
• Data Analysis and Data Mining, Big Data
• The R Programming Language
• Statistics, Mathematical Statistics, and SAS Programming

• Introduction to Data Science, with Introduction to R (Jeffrey Stanton)
• The Mirror Site (1) - PDF, ePub, Kindle, etc.
• The Mirror Site (2) - PDF (196 pages, 23.1 MB)
• The Mirror Site (3) - 2012 Edition - PDF (157 pages, 18.5 MB)

• Data Science: Theories, Models, Algorithms, and Analytics
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• R for Data Science: Visualize, Model, Transform, Tidy, and Import Data
• Exploring Data Science (Nina Zumel, et al)