Data Science for Engineers

This textbook provides a comprehensive introduction to data science, machine learning, and artificial intelligence, with conceptual understanding and mathematical details for practical engineering problems. It is written for undergraduate and senior undergraduate students in engineering disciplines.

Format: Hardback
Length: 344 pages
Publication date: 16 December 2022
Publisher: Taylor & Francis Ltd

With the remarkable surge in computational power and the abundance of rich data, nearly all engineering disciplines have incorporated data science to varying degrees. This comprehensive textbook offers a structured and comprehensive introduction to data science, encompassing the fields of machine learning and artificial intelligence. It provides a solid conceptual foundation, with just the right amount of mathematical intricacies, to aid readers in grasping the core themes of data science, machine learning, and artificial intelligence. This knowledge empowers readers to develop initial data science solutions for real-world engineering problems.

The book adopts a systematic approach to elucidate data science techniques, highlighting their ability to transcend boundaries across various disciplines. It delves into topics such as statistics, linear algebra, and optimization from a data science perspective, providing valuable insights. Numerous examples are provided to elucidate the underlying principles of machine learning algorithms, while also introducing several contemporary machine learning techniques.

Primarily designed for undergraduate and senior undergraduate students in diverse engineering disciplines, including chemical engineering, mechanical engineering, electrical engineering, electronics, and communications engineering, this textbook serves as a valuable resource for courses focusing on data science, machine learning, and artificial intelligence.

Weight: 656g
Dimension: 161 x 241 x 27 (mm)
ISBN-13: 9780367754266