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Data Science in Practice

  • Book
  • © 2019

Overview

Editors:
  1. Alan Said

    1. University of Skövde, Skövde, Sweden

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    You can also search for this editor in PubMed   Google Scholar

  2. Vicenç Torra

    1. University of Skövde, Skövde, Sweden

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    You can also search for this editor in PubMed   Google Scholar

  • Presents the latest research on Data Science in Practice
  • Especially focuses on the applications of Data Science and Big Data
  • Written by respected experts in the field

Part of the book series: Studies in Big Data (SBD, volume 46)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-viii
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  2. Data Science: An Introduction

    • Alan Said, Vicenç Torra
    Pages 1-6

  3. Concepts

    1. Front Matter

      Pages 7-7
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    2. Artificial Intelligence

      • Vicenç Torra, Alexander Karlsson, H. Joe Steinhauer, Stefan Berglund
      Pages 9-26

    3. Machine Learning: A Concise Overview

      • Denio Duarte, Niclas Ståhl
      Pages 27-58

  4. Application Domains

    1. Front Matter

      Pages 59-59
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    2. Information Fusion

      • H. Joe Steinhauer, Alexander Karlsson
      Pages 61-78

    3. Information Retrieval and Recommender Systems

      • Alejandro Bellogín, Alan Said
      Pages 79-96

    4. Business Intelligence

      • Carl Anderson
      Pages 97-118

  5. Tools

    1. Front Matter

      Pages 119-119
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    2. Data Privacy

      • Vicenç Torra, Guillermo Navarro-Arribas, Klara Stokes
      Pages 121-132

    3. Visual Data Analysis

      • Juhee Bae, Göran Falkman, Tove Helldin, Maria Riveiro
      Pages 133-155

    4. Complex Data Analysis

      • Juhee Bae, Alexander Karlsson, Jonas Mellin, Niclas Ståhl, Vicenç Torra
      Pages 157-169

    5. Big Data programming with Apache Spark

      • Elio Ventocilla
      Pages 171-194

  6. Back Matter

    Pages 195-195
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Keywords

About this book

This book approaches big data, artificial intelligence, machine learning, and business intelligence through the lens of Data Science. We have grown accustomed to seeing these terms mentioned time and time again in the mainstream media. However, our understanding of what they actually mean often remains limited. This book provides a general overview of the terms and approaches used broadly in data science, and provides detailed information on the underlying theories, models, and application scenarios. Divided into three main parts, it addresses what data science is; how and where it is used; and how it can be implemented using modern open source software. The book offers an essential guide to modern data science for all students, practitioners, developers and managers seeking a deeper understanding of how various aspects of data science work, and of how they can be employed to gain a competitive advantage.

Editors and Affiliations

  • University of Skövde, Skövde, Sweden

    Alan Said, Vicenç Torra

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