Graduate Degree

Data Science - Master of Science

Program:
Data Science - Master of Science
Plan Code:
DATASCI_MS
Program Level:
Graduate
Award Type:
Master of Science
College:
College of Natural Science
Department:
Statistics and Probability


Excerpt from the official Academic Programs Catalog:

Listed below are the approved requirements for the program from the official Academic Programs Catalog.
Students must consult their advisors to learn which specific requirements apply to their degree programs.


College of Natural Science

Department of Statistics and Probability

Graduate Study
Data Science - Master of Science

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:
  1. Have a four-year bachelor’s degree in a relevant quantitative discipline.
  2. 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 Science

A 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.