a Python approach to concepts, techniques and applications /
First Statement of Responsibility
Laura Igual, Santi Seguí ; with contributions from Jordi Vitrià [and others].
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Cham, Switzerland :
Name of Publisher, Distributor, etc.
Springer,
Date of Publication, Distribution, etc.
2017.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource
SERIES
Series Title
Undergraduate topics in computer science,
ISSN of Series
1863-7310
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and index.
CONTENTS NOTE
Text of Note
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.
0
SUMMARY OR ABSTRACT
Text of Note
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