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.

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.

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.