Pocket Primer Series Read Description

Data Science Fundamentals Pocket Primer

Paperback
May 2021
9781683927334
More details
  • Publisher
    Mercury Learning and Information
  • Published
    25th May 2021
  • ISBN 9781683927334
  • Language English
  • Pages 428 pp.
  • Size 6" x 9"
  •    Request Exam Copy
$59.95
E-Book

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May 2021
9781683927310
More details
  • Publisher
    Mercury Learning and Information
  • Published
    12th May 2021
  • ISBN 9781683927310
  • Language English
  • Pages 428 pp.
  • Size 6" x 9"
  •    Request E-Exam Copy
$59.95
Lib E-Book

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May 2021
9781683927327
More details
  • Publisher
    Mercury Learning and Information
  • Published
    12th May 2021
  • ISBN 9781683927327
  • Language English
  • Pages 428 pp.
  • Size 6" x 9"
$129.95

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 info@merclearning.com.

1: Working with Data
2: Introduction to Probability and Statistics
3: Linear Algebra Concepts
4: Introduction to Python
5: Introduction to NumPy
6: Introduction to Pandas
7: Introduction to R
8: Regular Expressions
9: SQL and NoSQL
10: Data Visualization
Index

Oswald Campesato

Oswald Campesato specializes in Deep Learning, Python, and GPT-4. He is the author/co-author of over forty books including Python 3 Data Visualization Using ChatGPT / GPT-4, NLP for Developers, and Artificial Intelligence, Machine Learning and Deep Learning (all Mercury Learning).

Computer Science; Data Analytics; Programming; Python; NumPy; R; SQL; NoSQL; Pandas