Amazon cover image
Image from Amazon.com

A hands-on introduction to data science / Chirag Shah.

By: Material type: TextTextLanguage: English Publication details: Cambridge ; New York, NY, : Cambridge University Press, 2020.Description: xxiii, 433 pages : illustrations, charts, tables (some color) ; 26 cm. ISBN:
  • 9781108472449
Subject(s): LOC classification:
  • QA76 .S469 2020
Contents:
Pt. 1. Conceptual introductions — 1. Introduction — 2. Data — 3. Techniques — Pt. 2. Tools for data science — 4. UNIX — 5. Python — 6. R — 7. MySQL — Pt. 3. Machine learning for data science — 8. Machine learning introduction and regression — 9. Supervised learning — 10. Unsupervised learning — Pt. 4. Applications, evaluations, and methods — 11. Hands-on with solving data problems — 12. Data collection, experimentation and evaluation.
Summary: This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.

Includes bibliographical references and index.

Pt. 1. Conceptual introductions — 1. Introduction — 2. Data — 3. Techniques — Pt. 2. Tools for data science — 4. UNIX — 5. Python — 6. R — 7. MySQL — Pt. 3. Machine learning for data science — 8. Machine learning introduction and regression — 9. Supervised learning — 10. Unsupervised learning — Pt. 4. Applications, evaluations, and methods — 11. Hands-on with solving data problems — 12. Data collection, experimentation and evaluation.

This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.

Powered by Koha