Neuware - 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.

N� de ref. del art�culo 9783319500164

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Detalles bibliogr�ficos

T�tulo: Introduction to Data Science : A Python Approach to Concepts, Techniques and Applications
Editor: Springer, Berlin, Springer International Publishing, Springer
Fecha de publicaci�n: 2017
Idioma: Ingl�s
ISBN 10: 3319500163
ISBN 13: 9783319500164
Encuadernaci�n: Taschenbuch
Estado: Neu

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Sinopsis

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.

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Dr. Laura Igual is an Associate Professor at the Departament de Matem�tiques i Inform�tica, Universitat de Barcelona, Spain. Dr. Santi Segu� is an Assistant Professor at the same institution.

The authors wish to mention that some chapters were co-written by Jordi Vitri�, Eloi Puertas, Petia Radeva, Oriol Pujol, Sergio Escalera, Francesc Dant� and Llu�s Garrido.

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