Librería Portfolio

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.

Almost everything in the book is accompanied with examples and practice - both in-chapter and end-of-chapter so students are more engaged because they can use hands-on experiences to see how theories relate to solving practical problems
Assumes no prior technical background or computing knowledge and lowers the barrier for entering the field of data science so that students from a range of disciplines can benefit from a more accessible introduction to data science
Supplemented by a generous set of material for instructors, including curriculum suggestions and syllabi, slides for each chapter, datasets, program scripts, answers and solutions to each exercise, as well as sample exams and projects which gives instructors end-to-end support for teaching a data science course



Table of Contents

Part I. Introduction:
1. Introduction
2. Data
3. Techniques
Part II. Tools:
4. UNIX
5. Python
6. R
7. MySQL
Part III. Machine Learning:
8. Machine learning introduction and regression
9. Supervised learning
10. Unsupervised learning
Part IV. Applications and Evaluations:
11. Hands-on with solving data problems
12. Data collection, experimentation and evaluation.