Data Science for Mathematicians
Seiten
2024
Chapman & Hall/CRC (Verlag)
978-0-367-52849-2 (ISBN)
Chapman & Hall/CRC (Verlag)
978-0-367-52849-2 (ISBN)
Mathematicians have skills that 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. This handbook will assist mathematicians to better understand the opportunities presented by data science.
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.
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.
Nathan Carter is a professor at Bentley University.
Contents
Chapter 1 Introduction 1
Chapter 2 Programming with Data
Chapter 3 Linear Algebra
Chapter 4 Basic Statistics
Chapter 5 Clustering
Chapter 6 Operations Research
Chapter 7 Dimensionality Reduction
Chapter 8 Machine Learning
Chapter 9 Deep Learning
Chapter 10 Topological Data Analysis
Bibliography
| Erscheinungsdatum | 21.08.2020 |
|---|---|
| Reihe/Serie | CRC Press/Chapman and Hall Handbooks in Mathematics Series |
| Zusatzinfo | 39 Tables, black and white; 151 Illustrations, black and white |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 1010 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Analysis |
| ISBN-10 | 0-367-52849-5 / 0367528495 |
| ISBN-13 | 978-0-367-52849-2 / 9780367528492 |
| Zustand | Neuware |
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