Pocket Primer Series Read Description

Dealing With Data Pocket Primer

Paperback
May 2022
9781683928201
More details
  • Publisher
    Mercury Learning and Information
  • Published
    12th May
  • ISBN 9781683928201
  • Language English
  • Pages 250 pp.
  • Size 6" x 9"
$41.95
E-Book (ePub)
May 2022
9781683928188
More details
  • Publisher
    Mercury Learning and Information
  • Published
    4th May
  • ISBN 9781683928188
  • Language English
  • Pages 250 pp.
  • Size 6" x 9"
$34.95
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May 2022
9781683928195
More details
  • Publisher
    Mercury Learning and Information
  • Published
    4th May
  • ISBN 9781683928195
  • Language English
  • Pages 250 pp.
  • Size 6" x 9"
$109.95

As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to the basic concepts of managing data using a variety of computer languages and applications. It is intended to be a fast-paced introduction to some basic features of data management and covers statistical concepts, data-related techniques, features of Pandas, RDBMS, SQL, NLP topics, Matplotlib, and data visualization. Companion files with source code and color figures are available.

FEATURES:

  • Covers Pandas, RDBMS, NLP, data cleaning, SQL, and data visualization
  • Introduces probability and statistical concepts
  • Features numerous code samples throughout
  • Includes companion files with source code and figures

1: Introduction to Probability and Statistics
2: Working with Data
3: Introduction to Pandas
4: Introduction to RDBMS and SQL
5: Working with SQL and MySQL
6: NLP and Data Cleaning
7: Data Visualization
Index

Oswald Campesato

Oswald Campesato (San Francisco, CA) is an adjunct instructor at UC-Santa Clara and specializes in Deep Learning, Java, Android, TensorFlow, and NLP. He is the author/co-author of over twenty-five books including TensorFlow 2 Pocket Primer, Python 3 for Machine Learning, and the NLP Using R Pocket Primer (all Mercury Learning and Information).

Pandas; RDBMS; NLP; data cleaning; SQL; data visualization; statistics; computer science; data analytics; python; programming