The Master of Science degree in Data Science is designed to provide students with an interdisciplinary blend of statistics, computer science, and computational science and mathematics which provides the necessary training to assimilate, process, analyze, and interpret data from diverse sources.
Admission
To be considered for admission to the master’s degree, a student must:
- Have a four-year bachelor’s degree in a relevant quantitative discipline.
- Demonstrate sufficient quantitative preparation through work or other relevant experiences.
In addition to meeting the requirements of the university and of the College of Natural Science, students must meet the requirements specified below.
Requirements for the Master of Science Degree in Data ScienceA total of 30 credits is required for the degree under Plan B (without thesis). The student’s program of study must be approved by the student’s guidance committee and must meet the requirements specified below.
|
|
|
|
|
|
|
|
|
|
| 1. |
All of the following courses (18 credits): |
|
|
CMSE |
830 |
Foundations of Data Science |
3 |
|
CMSE |
831 |
Computational Optimization |
3 |
|
CSE |
482 |
Big Data Analysis |
3 |
|
CSE |
881 |
Data Mining |
3 |
|
STT |
810 |
Mathematical Statistics for Data Scientists |
3 |
|
STT |
811 |
Applied Statistical Modeling for Data Scientists |
3 |
| 2. |
Complete 9 credits of elective courses from the following: |
|
|
CMSE |
402 |
Data Visualization Principles and Techniques |
3 |
|
CMSE |
822 |
Parallel Computing |
3 |
|
CMSE |
890 |
Selected Topics in Computational Mathematics, Science, and Engineering |
1 to 4 |
|
CSE |
802 |
Pattern Recognition and Analysis |
3 |
|
CSE |
830 |
Design and Theory of Algorithms |
3 |
|
CSE |
847 |
Machine Learning |
3 |
|
STT |
802 |
Statistical Computation |
3 |
|
STT |
812 |
Statistical Learning and Data Analysis |
3 |
|
STT |
873 |
Statistical Learning and Data Mining |
3 |
|
STT |
874 |
Introduction to Bayesian Analysis |
3 |
|
STT |
875 |
R Programming for Data Sciences |
3 |
|
CMSE 890 must be approved by the student’s guidance committee. |
|
|
Other courses may be available to fulfill this requirement with advisor approval. |
|
| 3. |
Completion of a 3-credit capstone course involving an applied, industrial, or governmental data science project. Students may complete this requirement by enrollment in Computer Science and Engineering 890, Computational Mathematics, Science, and Engineering 890, or Statistics and Probability 890. The student’s topic area must be approved by the student’s guidance committee. |
|
| 4. |
Completion of a final examination or evaluation. |
|