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Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications Second Edition 2024 Edition
Author(s): Laura Igual (Author), Santi Seguí (Author), Jordi Vitrià (Contributor), Eloi Puertas (Contributor), Petia Radeva (Contributor), Oriol Pujol (Contributor), Sergio Escalera (Contributor), Francesc Dantí (Contributor)
- Publisher: Springer
- Publication Date: April 13, 2024
- Edition: Second Edition 2024
- Language: English
- Print length: 260 pages
- ISBN-10: 3031489551
- ISBN-13: 9783031489556
Book Description
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.
Editorial Reviews
From the Back Cover
This textbook 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.
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.
About the Author
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.
