Data science for mathematicians
Mathematicians have skills that, if deepened in the right ways, would enable them to use data to answer questions important to them and others, and report those answers in compelling ways. Data science combines parts of mathematics, statistics, computer science. Gaining such power and the ability to teach has reinvigorated the careers of mathematicians. This handbook will assist mathematicians to better understand the opportunities presented by data science. As it applies to the curriculum, res…
Read More ↓Title Statements
- Full title :Data science for mathematicians / edited by Nathan Carter.
Summary
Mathematicians have skills that, if deepened in the right ways, would enable them to use data to answer questions important to them and others, and report those answers in compelling ways. Data science combines parts of mathematics, statistics, computer science. Gaining such power and the ability to teach has reinvigorated the careers of mathematicians. This handbook will assist mathematicians to better understand the opportunities presented by data science. As it applies to the curriculum, research, and career opportunities, data science is a fast-growing field. Contributors from both academics and industry present their views on these opportunities and how to advantage them.
Read More ↓Edition Statements
- Publication Date:2021
- Edition:First edition
Publication Statements
- Publisher(s):Chapman & Hall/CRC
- Place of Publication:Boca Raton, FL
- Language:eng
- Publication Date:2021
The Physical Object
- Description:1 online resource : : illustrations (black and white)
Edition Identifiers
- ISBN:9780429675683, 0429675682, 9780429398292, 0429398298, 9780429675669, 0429675666, 9780429675676, 0429675674
- DOI:10.1201/9780429398292
- oclc:on1196191610
- LC Classification:QA300
- Dewey Decimal:515
Table of Contents
Programming with data / Sean Raleigh -- Linear algebra / Jeffery Leader -- Basic statistics / David White -- Clustering / Amy S. Wagaman -- Operations research / Alice Paul and Susan Martonosi -- Dimensionality reduction / Sofya Chepushtanova, Elin Farnell, Eric Kehoe, Michael Kirby, and Henry Kvinge -- Machine learning / Mahesh Agarwal, Nathan Carter, and David Oury -- Deep learning / Samuel S. Watson -- Topological data analysis / Henry Adams, Johnathan Bush, Joshua Mirth.
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