From the Back Cover

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 bookSupports understanding through hands-on experience of solving data science problems using PythonDescribes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programmingReviews a range of applications of data science, including recommender systems and sentiment analysis of text dataProvides 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 Igualis an Associate Professor at theDepartament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain.Dr. Santi Seguíis an Assistant Professor at the same institution.

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About the Author

Dr. Laura Igualis an Associate Professor at theDepartament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain.Dr. Santi Seguíis an Assistant 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, Francesc Dantí and Lluís Garrido.

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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.