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Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications de Laura Igual

Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications: Undergraduate Topics in Computer Science

Autor Laura Igual, Santi Seguí Contribuţii de Jordi Vitrià, Eloi Puertas, Petia Radeva, Oriol Pujol, Sergio Escalera, Francesc Dantí
en Limba Engleză Paperback – 13 apr 2024
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.
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Specificații

ISBN-13: 9783031489556
ISBN-10: 3031489551
Pagini: 246
Ilustrații: XIV, 246 p. 82 illus., 78 illus. in color.
Dimensiuni: 155 x 235 x 19 mm
Greutate: 0.37 kg
Ediția:2nd ed. 2024
Editura: Springer International Publishing
Colecția Springer
Seria Undergraduate Topics in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

1. Introduction to Data Science.- 2. Toolboxes for Data Scientists.- 3. Descriptive statistics.- 4. Statistical Inference.- 5. Supervised Learning.- 6. Regression Analysis.- 7. Unsupervised Learning.- 8. Network Analysis.- 9. Recommender Systems.- 10. Statistical Natural Language Processing for Sentiment Analysis.- 11. Parallel Computing.

Notă biografică

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.

Textul de pe ultima copertă

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.

Caracteristici

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

Recenzii

“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)
“The book ‘Introduction to Data Science’ is built as a starter presentation of concepts, techniques and approaches that constitute the initial contact with data science for scientists … . The style of the book recommends it to both undergraduates and postgraduates and the concluding remarks and references provide guidance for the next steps in the study of particular topics.” (Irina Ioana Mohorianu, zbMATH, Vol. 1365.62003, 2017)