Data Science Fundamentals Pocket Primer

Campesato, Oswald

ISBN 10: 1683927338 / ISBN 13: 9781683927334

Editore: Mercury Learning and Information, 2021

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Dati bibliografici

• Titolo: Data Science Fundamentals Pocket Primer
• Editore: Mercury Learning and Information
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 1683927338
• ISBN 13: 9781683927334
• Rilegatura: Paperback
• Condizione: Fine

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

Campesato Oswald :

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).