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Sinopsis
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.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
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.
De la contraportada
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.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications (Undergraduate Topics in Computer Science)
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Introduction to Data Science
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Springer International Publishing AG, 2024
ISBN 10: 3031489551
ISBN 13: 9783031489556
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Introduction to Data Science
Publicado por
Springer International Publishing AG, 2024
ISBN 10: 3031489551
ISBN 13: 9783031489556
Nuevo
PAP
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000. Nº de ref. del artículo: GB-9783031489556
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INTRODUCTION TO DATA SCIENCE : A PYTHON
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Introduction to Data Science
Publicado por
Springer International Publishing AG, CH, 2024
ISBN 10: 3031489551
ISBN 13: 9783031489556
Nuevo
Paperback
Librería: Rarewaves.com UK, London, Reino Unido
Calificación del vendedor: 4 de 5 estrellas
Paperback. Condición: New. 2nd ed. 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 scienceReviews 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. Nº de ref. del artículo: LU-9783031489556
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Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications (Undergraduate Topics in Computer Science)
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Introduction to Data Science
Publicado por
Springer International Publishing AG, CH, 2024
ISBN 10: 3031489551
ISBN 13: 9783031489556
Nuevo
Paperback
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
Calificación del vendedor: 4 de 5 estrellas
Paperback. Condición: New. 2nd ed. 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 scienceReviews 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. Nº de ref. del artículo: LU-9783031489556
Cantidad disponible: 1 disponibles
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Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications
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Springer-Nature New York Inc, 2024
ISBN 10: 3031489551
ISBN 13: 9783031489556
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Paperback. Condición: Brand New. 2nd edition. 246 pages. 9.00x5.75x0.50 inches. In Stock. This item is printed on demand. Nº de ref. del artículo: __3031489551
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Introduction to Data Science
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Springer, Berlin, Springer International Publishing, Springer, 2024
ISBN 10: 3031489551
ISBN 13: 9783031489556
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the interdisciplinary field of data science. The coverage spans key conceptsfrom statistics, machine/deeplearningand responsible data science,useful techniques fornetworkanalysis andnatural language processing,and practical applicationsof data science such as recommender systems or sentiment analysis.Topics and features:Provides numerous practical case studies using real-world data throughout the bookSupports understanding through hands-on experience of solving data science problems using PythonDescribesconcepts,techniques and tools for statistical analysis, machine learning, graph analysis,natural language processing, deep learning andresponsible data scienceReviews a range of applications of data science, including recommender systems and sentiment analysis of text dataProvides supplementary code resources and data at an associated websiteThis 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. 246 pp. Englisch. Nº de ref. del artículo: 9783031489556
Cantidad disponible: 2 disponibles
Imagen del vendedor
Introduction to Data Science : A Python Approach to Concepts, Techniques and Applications
Publicado por
Springer International Publishing, 2024
ISBN 10: 3031489551
ISBN 13: 9783031489556
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the interdisciplinary field of data science. The coverage spans key conceptsfrom statistics, machine/deeplearningand responsible data science,useful techniques fornetworkanalysis andnatural language processing,and practical applicationsof data science such as recommender systems or sentiment analysis.Topics and features:Provides numerous practical case studies using real-world data throughout the bookSupports understanding through hands-on experience of solving data science problems using PythonDescribesconcepts,techniques and tools for statistical analysis, machine learning, graph analysis,natural language processing, deep learning andresponsible data scienceReviews a range of applications of data science, including recommender systems and sentiment analysis of text dataProvides supplementary code resources and data at an associated websiteThis 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. Nº de ref. del artículo: 9783031489556
Cantidad disponible: 1 disponibles