Springer Series in the Data Sciences

Mathematical Foundations for Data Analysis

Authors: Phillips, Jeff M.

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  • Provides accessible, simplified introduction to core mathematical language and concepts
  • Integrates examples of key concepts through geometric illustrations and Python coding
  • Addresses topics in locality sensitive hashing, graph-structured data, and big data processing as well as basic linear algebra
  • Includes perspectives on ethics in data
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eBook $54.99
price for USA in USD
  • ISBN 978-3-030-62341-8
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $69.99
price for USA in USD
  • ISBN 978-3-030-62340-1
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
About this Textbook

This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra.  Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.

About the authors

Jeff M. Phillips is an Associate Professor in the School of Computing within the University of Utah.  He directs the Utah Center for Data Science as well as the Data Science curriculum within the School of Computing.  His research is on algorithms for big data analytics, a domain with spans machine learning, computational geometry, data mining, algorithms, and databases, and his work regularly appears in top venues in each of these fields.  He focuses on a geometric interpretation of problems, striving for simple, geometric, and intuitive techniques with provable guarantees and solve important challenges in data science.  His research is supported by numerous NSF awards including an NSF Career Award.


Table of contents (11 chapters)

Table of contents (11 chapters)

Buy this book

eBook $54.99
price for USA in USD
  • ISBN 978-3-030-62341-8
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $69.99
price for USA in USD
  • ISBN 978-3-030-62340-1
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
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Bibliographic Information

Bibliographic Information
Book Title
Mathematical Foundations for Data Analysis
Authors
Series Title
Springer Series in the Data Sciences
Copyright
2021
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-62341-8
DOI
10.1007/978-3-030-62341-8
Hardcover ISBN
978-3-030-62340-1
Series ISSN
2365-5674
Edition Number
1
Number of Pages
XVII, 287
Number of Illustrations
1 b/w illustrations, 108 illustrations in colour
Topics