Free Data Science eBooks – May 2018

If you're into R or Python, this months FREE eBooks should really hit the spot. They're really proving to be very popular.

On the other hand, maybe you're already an accomplished Data Science programmer and you're looking to improve your mathematics skills. If so, how about a book about differential equations? We've got one of those too!


I hope the 3 free books in this blog post prove to be a valuable resource to you and that you will visit regularly (and share with your friends in social media too).

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Free Data Science Ebooks May 2018

This month we highlight 3 books:

  • R Programming for Data Science
  • Dive into Python 3
  • Elementary Differential Equations

They're all FREE, so help yourselves...


R Programming for Data Science

This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. The skills taught in this book will lay the foundation for you to begin your journey learning data science.

Dive into Python 3

by Mark Pilgrim

Dive into Python 3

Dive Into Python 3 is a hands-on guide to Python 3 and its differences from Python 2. As in the original book, Dive Into Python, each chapter starts with a real, complete code sample, proceeds to pick it apart and explain the pieces, and then puts it all back together in a summary at the end

This book includes:

  • Example programs completely rewritten to illustrate powerful new concepts now available in Python 3: sets, iterators, generators, closures, comprehensions, and much more
  • A detailed case study of porting a major library from Python 2 to Python 3
  • A comprehensive appendix of all the syntactic and semantic changes in Python 3

This is the perfect resource for you if you need to port applications to Python 3, or if you like to jump into languages fast and get going right away.

Elementary Differential Equations

This text has been written in clear and accurate language that students can read and comprehend.

The author has minimized the number of explicitly state theorems and definitions, in favour of dealing with concepts in a more conversational manner. This is illustrated by over 250 worked out examples. The problems are extremely high quality and are regarded as one of the text's many strengths.

This book also allows the instructor to select the level of technology desired. Trench has simplified this by using the symbols C and L. C exercises call for computation and/or graphics, and L exercises are laboratory exercises that require extensive use of technology.

Several sections include informal advice on the use of technology. The instructor who prefers not to emphasize technology can ignore these exercises.

3 free data science ebooks for May #datascience
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