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Mathematics of big data: spreadsheets, databases, matrices, and graphs

By: Kepner, Jeremy.
Contributor(s): Hayden, Jananthan [Co author].
Material type: materialTypeLabelBookPublisher: London MIT Press 2018Description: xxi, 418p.ISBN: 9780262038393.Subject(s): Computer Science | Big data - Graphic methods | Graph Analysis and Machine Learning | Visualizing AlgebraDDC classification: 005.7 Summary: Mathematics of Big Data presents a sophisticated view of matrices, graphs, databases, and spreadsheets, with many examples to help the discussion. The authors present the topic in three parts-applications and practice, mathematical foundations, and linear systems—with self-contained chapters to allow for easy reference and browsing. The algorithms are expressed in D4M, with execution possible in Matlab, Octave, and Julia. With exercises at the end of each section, the book can be used as a supplemental or primary text for a class on big data, algorithms, data structures, data analytics, linear algebra, or abstract algebra. https://mitpress.mit.edu/books/mathematics-big-data
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Slot 83 (0 Floor, West Wing) Non-fiction 005.7 K3M2 (Browse shelf) Checked out 08/11/2019 198729

Mathematics of Big Data presents a sophisticated view of matrices, graphs, databases, and spreadsheets, with many examples to help the discussion. The authors present the topic in three parts-applications and practice, mathematical foundations, and linear systems—with self-contained chapters to allow for easy reference and browsing. The algorithms are expressed in D4M, with execution possible in Matlab, Octave, and Julia. With exercises at the end of each section, the book can be used as a supplemental or primary text for a class on big data, algorithms, data structures, data analytics, linear algebra, or abstract algebra.

https://mitpress.mit.edu/books/mathematics-big-data

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