About this book
This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis.
Topics and features:
- Provides numerous practical case studies using real-world data throughout the book
- Supports understanding through hands-on experience of solving data science problems using Python
- Describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming
- Reviews a range of applications of data science, including recommender systems and sentiment analysis of text data
- Provides supplementary code resources and data at an associated website
This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses.
Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain. Dr. Santi Seguí is an Assistant Professor at the same institution.
- Book Title Introduction to Data Science
- Book Subtitle A Python Approach to Concepts, Techniques and Applications
- Series Title Undergraduate Topics in Computer Science
- Series Abbreviated Title Undergraduate Topics Computer Sci.
- DOI https://doi.org/10.1007/978-3-319-50017-1
- Copyright Information Springer International Publishing Switzerland 2017
- Publisher Name Springer, Cham
- eBook Packages Computer Science Computer Science (R0)
- Softcover ISBN 978-3-319-50016-4
- eBook ISBN 978-3-319-50017-1
- Series ISSN 1863-7310
- Series E-ISSN 2197-1781
- Edition Number 1
- Number of Pages XIV, 218
- Number of Illustrations 6 b/w illustrations, 67 illustrations in colour
Data Mining and Knowledge Discovery
Probability and Statistics in Computer Science
Statistics and Computing/Statistics Programs
- Buy this book on publisher's site
“This book contains a broad range of timely topics and presents interesting examples on real-life data using Python. … the book is a good addition to references on Python and data science. Students as well as practicing data scientists and engineers will benefit from the many techniques and use cases presented in the book.” (Computing Reviews, December, 2017)