CSC 148 - Algorithms for Data Science

Semester Hours: 3

The course covers a variety of topics used in exploratory data analysis. Students will learn  how to discover patterns and trends in data that influence future modeling decisions. Examples of algorithms include: naïve Bayes  classifier, k-nearest neighbor classifier, k-means clustering algorithm, regression by the gradient descent, backpropagation training algorithm for neural networks and their application to data science. The course will  cover in detail the mathematical theory behind the algorithms: how they work and why  they terminate, and also how efficient they are. The course has a lab component devoted to programming the algorithms in Python and using them in data science applications. 

Prerequisite(s)/Course Notes:
MATH 072  and CSC 016  

View Course Offering(s):

Fall 2025

January 2026

Spring 2026