9783319500164-3319500163-Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications (Undergraduate Topics in Computer Science)

Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications (Undergraduate Topics in Computer Science)

ISBN-13: 9783319500164
ISBN-10: 3319500163
Edition: 2017
Author: Laura Igual, Santi Seguí
Publication date: 2017
Publisher: Springer
Format: Paperback 232 pages
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Book details

ISBN-13: 9783319500164
ISBN-10: 3319500163
Edition: 2017
Author: Laura Igual, Santi Seguí
Publication date: 2017
Publisher: Springer
Format: Paperback 232 pages

Summary

Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications (Undergraduate Topics in Computer Science) (ISBN-13: 9783319500164 and ISBN-10: 3319500163), written by authors Laura Igual, Santi Seguí, was published by Springer in 2017. With an overall rating of 4.5 stars, it's a notable title among other AI & Machine Learning (Computer Science) books. You can easily purchase or rent Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications (Undergraduate Topics in Computer Science) (Paperback, Used) from BooksRun, along with many other new and used AI & Machine Learning books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.33.

Description

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.

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