Data Science for Mathematicians CRC Press/Chapman and Hall Handbooks in Mathematics Series
Coordonnateur : Carter Nathan
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.
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
Date de parution : 09-2020
15.6x23.4 cm
Thèmes de Data Science for Mathematicians :
Mots-clés :
Persistent Homology; Big Data; Interior Point Methods; Data Analysis; Simple Recurrent Neural Network; Data Collection; Primal Dual Interior Point Algorithm; Computer science; TDA; Data science; Stochastic Gradient Descent; Linear algebra; Roc Curve; Mathematicians; Agent Based Modeling; Basic statistics; Metric Space; Gradient Descent; QR Decomposition; Boston Housing Dataset; LU Decomposition; Average Silhouette Width; Linearly Independent; Convolutional Layer; Klein Bottle; Version Control; Deep Learning Framework; Column Stochastic Matrix; Clustering Solution; Boston Housing Data; Fractal Dimension; Distributed Version Control System; Multilayer Perceptron