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- Author: Oswald Campesato
- Publisher: Mercury Learning and Information
- ISBN-10: 1683927338
- ISBN-13: 9781683927334
- Format: 21.1 x 28.2 x 3.1 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Description
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
EXTRA 10 % discount with code: EXTRA
The promotion ends in 22d.17:59:53
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Oswald Campesato
- Publisher: Mercury Learning and Information
- ISBN-10: 1683927338
- ISBN-13: 9781683927334
- Format: 21.1 x 28.2 x 3.1 cm, minkšti viršeliai
- Language: English English
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
Reviews