| CMP120 |
Introduction to Programming
An introduction to the theory and practice of computer programming with an emphasis on problem solving. No previous programming experience is required.
|
3 |
| DSA250 |
Fundamentals of Data Science
In this course students learn the fundamentals of the data science process, including data acquisition, data cleaning and manipulation to prepare for analysis, common machine learning models for classification and regression, unsupervised machine learning models, and principles of model evaluation.
Pre-requisites
Complete all 2 of the following courses:
CMP120 Introduction to Programming
MTH110 Elementary Statistics
|
3 |
| DSA411 |
Machine Learning and AI
An introduction to machine learning and artificial intelligence. Topics include classification, regression, clustering, planning, and scheduling. Includes current issues relevant to big data problems.
Pre-requisites
Complete the following course:
DSA250 Fundamentals of Data Science
|
3 |
| MTH110 |
Elementary Statistics
Topics include statistical measures and distributions, decision making under uncertainty, application of probability to statistical inference, linear correlation, introduction to nonparametric statistical methods, and application to problems drawn from the natural and social sciences. Three hours of class per week. Three hours of class per week.
|
3 |
| MTH151 |
Calculus I
This is the first course in the calculus sequence. Topics include differential and integral calculus for algebraic and trigonometric functions with applications. Four hours of class per week.
|
4 |
| MTH244 |
Discrete Mathematics
This course is an introduction to the fundamental logic and mathematical concepts of discrete quantities, as employed in digital computers. Emphasis will be on the careful and precise expression of ideas. Topics include sets and logic, relations and functions, proof techniques, algorithms, combinatorics, discrete probability, graphs, and trees. Three hours of class per week.
|
3 |
| COURSE CATALOG