A hands-on introduction to data science
Shah, Chirag
A hands-on introduction to data science - UK Cambridge University Press 2021 - xxiii, 433 p.
Frontmatter
Part I: - Conceptual Introductions
pp 1-2
1 - Introduction
pp 3-36
2 - Data
pp 37-65
3 - Techniques
pp 66-96
Part II: - Tools for Data Science
pp 97-98
4 - UNIX
pp 99-124
5 - Python
pp 125-160
6 - R
pp 161-186
7 - MySQL
pp 187-206
Part III: - Machine Learning for Data Science
pp 207-208
8 - Machine Learning Introduction and Regression
pp 209-234
9 - Supervised Learning
pp 235-289
10 - Unsupervised Learning
pp 290-318
Part IV: - Applications, Evaluations, and Methods
pp 319-320
11 - Hands-On with Solving Data Problems
pp 321-353
12 - Data Collection, Experimentation, and Evaluation
pp 354-378
Appendices
pp 379-417
Index
Description
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.
9781108472449
Computer science
Information technology
004 / SHA
A hands-on introduction to data science - UK Cambridge University Press 2021 - xxiii, 433 p.
Frontmatter
Part I: - Conceptual Introductions
pp 1-2
1 - Introduction
pp 3-36
2 - Data
pp 37-65
3 - Techniques
pp 66-96
Part II: - Tools for Data Science
pp 97-98
4 - UNIX
pp 99-124
5 - Python
pp 125-160
6 - R
pp 161-186
7 - MySQL
pp 187-206
Part III: - Machine Learning for Data Science
pp 207-208
8 - Machine Learning Introduction and Regression
pp 209-234
9 - Supervised Learning
pp 235-289
10 - Unsupervised Learning
pp 290-318
Part IV: - Applications, Evaluations, and Methods
pp 319-320
11 - Hands-On with Solving Data Problems
pp 321-353
12 - Data Collection, Experimentation, and Evaluation
pp 354-378
Appendices
pp 379-417
Index
Description
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.
9781108472449
Computer science
Information technology
004 / SHA