Introduction to data science : a Python approach to concepts, techniques and applications / Laura Igual, Santi Seguí ; with contributions from Jordi Vitrià... [et al.]

By: Igual, LauraContributor(s): Seguí, Santi | Ohio Library and Information NetworkMaterial type: TextTextSeries: Undergraduate topics in computer sciencePublisher: Cham, Switzerland : Springer, 2017Description: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319500171; 3319500171Subject(s): Quantitative research | Python (Computer program language)Genre/Form: Electronic books. Additional physical formats: No titleDDC classification: 001.4/2 LOC classification: Q180.55.Q36Online resources: OhioLINK Connect to resource | SpringerLink Connect to resource | SpringerLink Connect to resource (off-campus)
Contents:
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
Summary: 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< 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 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
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e-Books e-Books Main Library -University of Zimbabwe
Click on Online resources to access the e-Book Q180.55.Q36 (Browse shelf (Opens below)) Available

Includes bibliographical references and index

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

Available to OhioLINK libraries

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< 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 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

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