Overview
- Describes tools and techniques that demystify data science
- Discusses Python extensions, techniques and modules to perform statistical analysis and machine learning
- Includes case studies, and supplies code examples and data at an associated website
Part of the book series: Undergraduate Topics in Computer Science (UTICS)
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About this book
This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the interdisciplinary field of data science. The coverage spans key concepts from statistics, machine/deep learning and responsible data science, useful techniques for network analysis and natural language processing, and practical applications of data science such as recommender systems or 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 concepts, techniques and tools for statistical analysis, machine learning, graph analysis, natural language processing, deep learning and responsible data science
- 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.
Keywords
Table of contents (12 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
About the authors
Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain. Dr. Santi Seguí is an Associate Professor at the same institution.
The authors wish to mention that some chapters were co-written by Jordi Vitrià, Eloi Puertas, Petia Radeva, Oriol Pujol, Sergio Escalera.
Bibliographic Information
Book Title: Introduction to Data Science
Book Subtitle: A Python Approach to Concepts, Techniques and Applications
Authors: Laura Igual, Santi Seguí
Series Title: Undergraduate Topics in Computer Science
DOI: https://doi.org/10.1007/978-3-031-48956-3
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
Softcover ISBN: 978-3-031-48955-6Published: 13 April 2024
eBook ISBN: 978-3-031-48956-3Published: 12 April 2024
Series ISSN: 1863-7310
Series E-ISSN: 2197-1781
Edition Number: 2
Number of Pages: XIV, 246
Number of Illustrations: 4 b/w illustrations, 78 illustrations in colour
Topics: Data Structures and Information Theory, Artificial Intelligence, Data Mining and Knowledge Discovery, Python