Data Science Concentration
- To combine mathematics, statistics, and computing into an integrated curriculum, providing students the rigorous theoretical background of data science methods and techniques necessary for success in data-related fields in addition to just knowing how to implement them; and
- To synthesize the analytical skills and mathematical knowledge to analyze data, draw conclusions, and make decisions in real-life situations.
Requirements
| Concentration Required Courses | 6 units | |
| MATH3836 Data Mining | 3 units | |
| MATH4225 Foundation of Big Data and Learning | 3 units | |
| Concentration Elective Courses | 15 units | |
| Group A | ||
| Choose at least two courses of the following courses: | ||
| MATH4226 Introduction to Deep Learning | 3 units | |
| MATH4227 Programming for Data Science | 3 units | |
| MATH4815 Interior Point Methods for Convex Optimization | 3 units | |
| MATH4816 Optimization Theory and Techniques | 3 units | |
| Group B | ||
| Choose at least one course of the following courses: | ||
| MATH3427 Real Analysis | 3 units | |
| MATH3605 Numerical Methods II | 3 units | |
| MATH3615 Introduction to Imaging Science | 3 units | |
| MATH3626 Computational Statistics for Data Science | 3 units | |
| MATH3826 Markov Chain and Queuing Theory | 3 units | |
| MATH4615 Introduction to Numerical Linear Algebra | 3 units | |
| MATH4807 Categorical Data Analysis | 3 units | |
| MATH4826 Time Series and Forecasting | 3 units | |
| MATH4875 Special Topics in Statistics I | 3 units | |
| MATH4876 Special Topics in Statistics II | 3 units | |
| MATH4877 Special Topics in Statistics III | 3 units | |
| 21 units | ||
