Introduction to Matrices and Graphs in Data Science
This connector will cover introductory topics in the mathematics of data science, focusing on discrete probability and linear algebra and the connections between them that are useful in modern theory and practice. We will focus on matrices and graphs as popular mathematical structures with which to model data. For examples, as models for term-document corpora, high-dimensional regression problems, ranking/classification of web data, adjacency properties of social network data, etc.This course connects to the Foundations of Data Science course by providing a unified view of the mathematical methods that underlie the theoretical foundations of data science. Typically, these methods are taught from a statistical perspective, or they are taught from a computer science perspective, or they are taught from a purely mathematical perspective. This connector course will try to drill down…
Link to Full Article: Introduction to Matrices and Graphs in Data Science
Sharing
