This area is not discussed as often in data science, but all modern data science is done with the help of computational systems, and discrete math is at the heart of such systems. A refresher in discrete math will include concepts critical to daily use of algorithms and data structures in analytics project:

- Sets, subsets, power sets

- Counting functions, combinatorics, countability

- Basic proof techniques: induction, proof by contradiction

- Basics of inductive, deductive, and propositional logic

- Basic data structures: stacks, queues, graphs, arrays, hash tables, trees

- Graph properties: connected components, degree, maximum flow/minimum cut concepts, graph coloring

- Recurrence relations and equations

- Growth of functions and \(O(n)\) notation concept

**Where You Might Use It**

In any social network analysis, you need to know the properties of a graph and fast algorithm to search and traverse the network. In any choice of algorithm, you need to understand the time and space complexity - i.e., how the running time and space requirement grows with input data size, by using \(O(n)\) (Big-Oh) notation.