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Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications, ISBN-13: 978-3319500164

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Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications, ISBN-13: 978-3319500164

[PDF eBook eTextbook]

  • Publisher: ‎ Springer; 1st ed. 2017 edition (March 2, 2017)
  • Language: ‎ English
  • 232 pages
  • ISBN-10: ‎ 3319500163
  • ISBN-13: ‎ 978-3319500164

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.

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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