Data Science Fundamentals Pocket Primer
eBook - ePub

Data Science Fundamentals Pocket Primer

  1. 428 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Data Science Fundamentals Pocket Primer

Book details
Table of contents
Citations

About This Book

As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to the basic concepts of data science using Python 3 and other computer applications. It is intended to be a fast-paced introduction to some basic features of data analytics and also covers statistics, data visualization, linear algebra, and regular expressions. The book includes numerous code samples using Python, NumPy, R, SQL, NoSQL, and Pandas. Companion files with source code and color figures are available. FEATURES:

  • Includes a concise introduction to Python 3 and linear algebra
  • Provides a thorough introduction to data visualization and regular expressions
  • Covers NumPy, Pandas, R, and SQL
  • Introduces probability and statistical concepts
  • Features numerous code samples throughout
  • Companion files with source code and figures

The companion files are available online by emailing the publisher with proof of purchase at [email protected].

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Yes, you can access Data Science Fundamentals Pocket Primer by Oswald Campesato in PDF and/or ePUB format, as well as other popular books in Computer Science & Programming in Python. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Mercury Learning and Information
Year
2021
ISBN
9781683927310
Topic
Computer Science
Subtopic
Programming in Python
Index
Computer Science

Table of contents

  1. Cover page
  2. Title page
  3. Copyright
  4. Dedication
  5. Contents
  6. Preface
  7. Chapter 1 Working With Data
  8. Chapter 2 Intro to Probability and Statistics
  9. Chapter 3 Linear Algebra Concepts
  10. Chapter 4 Introduction to Python
  11. Chapter 5 Introduction to NumPy
  12. Chapter 6 Introduction to Pandas
  13. Chapter 7 Introduction to R
  14. Chapter 8 Regular Expressions
  15. Chapter 9 SQL and NoSQL
  16. Chapter 10 Data Visualization
  17. Index