The program requires courses specific to the area of statistics and data science, and the student is required to finish ten 4-credit courses and two 1-credit capstone seminars for a total of 42 credits. An exit exam or final thesis is not required.
Sample Schedule
| Fall Semester of the 1st Year (12 credits) |
| Math 501: Probability (4) Math 530: Computational Linear Algebra (4) Math 531: Statistical Modeling with Regression (4) |
| Spring Semester of the 1st Year (12 credits) |
| Math 502: Statistical Inference (4) Math 532: Generalized Linear & Mixed Models (4) Math 535 or Math 570: Advanced Statistical Learning or Data Mining with Multivariate Analysis (4) * |
| Fall Semester of the 2nd Year (9 credits) |
| Elective Course #1 (4) Elective Course #2 (4) Math 540: Capstone Seminar I (1) |
| Spring Semester of the 2nd Year (9 credits) |
| Elective Course #3 (4) Elective Course #4 (4) Math 541: Capstone Seminar II (1) |
* If both Math 535 and Math 570 are completed, one will satisfy the required course requirement while the other will be counted as an elective.
Grade Requirements
In addition to the course requirements (24 credits from 6 core courses, 16 credits from 4 elective courses, and 2 credits from 2 capstone seminars), the student must maintain at least a B average (GPA 3.0) and a minimum grade not lower than B– in these courses. In addition, the student must have a minimum grade not lower than B in the two capstone seminars.
Course Catalog
|
Core Courses (24 credits) |
|||
|
Course number |
Title |
Credits |
Semester |
|
Math 501 |
Probability |
4 |
1F |
|
Math 502 |
Statistical Inference |
4 |
1S |
|
Math 530 |
Computational Linear Algebra |
4 |
1F |
|
Math 531 |
Statistical Modeling with Regression |
4 |
1F |
|
Math 532 |
Generalized Linear & Mixed Models |
4 |
1S |
|
Math 535 / Math 570 |
Advanced Statistical Learning / Data Mining with Multivariate Analysis |
4 |
1S |
|
Elective Courses (16 credits from any 4 courses below) |
|||
|
Course number |
Title |
Credits |
Semester |
|
Math 534 |
Practical Data Analysis |
4 |
2* |
|
Math 536 |
Nonparametric Smoothing and Semiparametric Regression |
4 |
2* |
|
Math 537 |
Reliability |
4 |
2* |
|
Math 538 |
Sequential Analysis |
4 |
2* |
|
Math 553 |
Nonparametric Inference |
4 |
2* |
|
Math 554 |
Sampling Theory |
4 |
2* |
|
Math 556 |
Design of Experiments |
4 |
2* |
|
Math 557 |
Survival Analysis |
4 |
2* |
|
Math 559 |
Time Series Analysis |
4 |
2* |
|
Math 573 |
Applied Probability and Stochastic Processes |
4 |
2* |
|
Capstone Seminars (2 credits) |
|||
|
Course number |
Title |
Credits |
Semester |
|
Math 540 |
Capstone |
1 |
2F |
|
Math 541 |
Capstone |
1 |
2S |
|
Doctoral Level Courses (may be taken as electives) |
|||
|
Course number |
Title |
Credits |
Semester |
|
Math 555 |
Linear Models |
4 |
|
|
Math 558 |
Multivariate Statistical Analysis |
4 |
|
|
Math 571 |
Advanced Probability Theory |
4 |
|
|
Math 572 |
Stochastic Processes |
4 |
|
|
Math 579 |
Advanced Statistical Inference |
4 |
|
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