A hands-on introduction to data science

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.