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Introduction to Data Science

A Python Approach to Concepts, Techniques and Applications

Author(s):
Publisher:

Springer

Pages: 218
Further Actions:

Recommend to library

AVAILABLE FORMATS

Paperback - 9783319500164

04 March 2017

€49.04

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Ebook - 9783319500171

22 February 2017

€35.69

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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...

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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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Describes tools and techniques that demystify data science

Presents a focus on analytical techniques; the core toolbox for every data scientist

Includes numerous practical case studies using real-world data, supplying code examples and data at an associated website

Discusses Python extensions, techniques and modules to perform statistical analysis and machine learning, and important applications of data science

Introduction to Data Science.- Toolboxes for Data Scientists.- Descriptive statistics.- Statistical Inference.- Supervised Learning.- Regression Analysis.- Unsupervised Learning.- Network Analysis.- Recommender Systems.- Statistical Natural Language Processing for Sentiment Analysis.- Parallel Computing.

“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)
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Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain. Dr. Santi Seguí is 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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Dr. Laura Igual is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain. Dr. Santi Seguí is 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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