Introduction to Data Science

A Python Approach to Concepts, Techniques and Applications

Nonfiction, Computers, Database Management, Application Software, General Computing
Cover of the book Introduction to Data Science by Petia Radeva, Oriol Pujol, Jordi Vitrià, Sergio Escalera, Santi Seguí, Francesc Dantí, Laura Igual, Lluís Garrido, Eloi Puertas, Springer International Publishing
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Author: Petia Radeva, Oriol Pujol, Jordi Vitrià, Sergio Escalera, Santi Seguí, Francesc Dantí, Laura Igual, Lluís Garrido, Eloi Puertas ISBN: 9783319500171
Publisher: Springer International Publishing Publication: February 22, 2017
Imprint: Springer Language: English
Author: Petia Radeva, Oriol Pujol, Jordi Vitrià, Sergio Escalera, Santi Seguí, Francesc Dantí, Laura Igual, Lluís Garrido, Eloi Puertas
ISBN: 9783319500171
Publisher: Springer International Publishing
Publication: February 22, 2017
Imprint: Springer
Language: English

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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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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