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© 2017

Introduction to Data Science

A Python Approach to Concepts, Techniques and Applications

  • 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

Textbook

Part of the Undergraduate Topics in Computer Science book series (UTICS)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Laura Igual, Santi Seguí
    Pages 1-4
  3. Laura Igual, Santi Seguí
    Pages 5-28
  4. Laura Igual, Santi Seguí
    Pages 29-50
  5. Laura Igual, Santi Seguí
    Pages 51-65
  6. Laura Igual, Santi Seguí
    Pages 67-96
  7. Laura Igual, Santi Seguí
    Pages 97-114
  8. Laura Igual, Santi Seguí
    Pages 115-139
  9. Laura Igual, Santi Seguí
    Pages 141-164
  10. Laura Igual, Santi Seguí
    Pages 165-179
  11. Laura Igual, Santi Seguí
    Pages 181-197
  12. Laura Igual, Santi Seguí
    Pages 199-215
  13. Back Matter
    Pages 217-218

About this book

Introduction

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.

Keywords

Data science Parallel computing Python programming Statistical inference Graph analysis

Authors and affiliations

  1. 1.Departament de Matemàtiques i InformàticaUniversitat de BarcelonaBarcelonaSpain
  2. 2.Departament de Matemàtiques i InformàticaUniversitat de BarcelonaBarcelonaSpain

About the authors

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.

Bibliographic information

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Reviews

“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)