Data Science - Master of Science (M.S.)

Master of Science (M.S.) - Data Science

College of Humanities and Sciences

Degree Specific Credits:

Thesis: 30 credits

Nonthesis: 36 credits

Required Cumulative GPA: 3.0

Catalog Year: 2022-23

Notes/Description

  • At least half of the credits required for a degree (excluding a combined total of 10 credits for thesis or research) will be at the 500 or 600 level. (In no case, however, will this rule require more than 18 credits of 500- or 600-level work.) To apply this rule to the course of study, subtract the number of thesis and research credits completed (up to 10 only) from the minimum number of credits required for the degree.

  • Half of the remaining credits must be in courses at the 500 or 600 level.

  • The student and the student's advisor design a program of studies for each student. Each year the student must complete (or update) an advisor-approved Program of Studies form which is to be kept on file in the Mathematics office. A revised form must be filed if there are any changes to the student's program during the year.

  • After the first year students will take a comprehensive exam on material from M 540, M 561, and M 562. It is structured in two parts, written and preliminary.

Summary

Core Course Requirements

18

Additional Courses

12-18

Total Hours

30-36


Core Course Requirements

Code

Title

Hours

Complete all of the following courses:

M 540

Numerical Methods for Computational & Data Science

3

M 561

Advanced Data Science Analytics

3

M 562

Advanced Theoretical Big Data Analytics

3

M 567

Advanced Data Science Projects

3

M 600

Math Colloquium

1

M 610 Or

STAT 640

Graduate Seminar in Applied Mathematics or Graduate Seminar in Probability and Statistics

2

Complete one course in CSCI (see courses below)

3

Total Hours

18

Minimum Required Grade: C

Additional Course Requirements

Code

Title

Hours

Complete additional credit requirements with the following courses:

CSCI 444

Data Visualization

3

CSCI 547

Machine Learning

3

CSCI 548

Pattern Recognition

3

CSCI 564

Applications Mining Big Data

3

CSCI 580

Applied Parallel Computing Techniques

3

STAT 421

Probability Theory

3

STAT 422

Mathematical Statistics

3

STAT 542

Applied Linear Models

3

STAT 543

Applied Multivariate Statistical Analysis

4

STAT 545

Theory of Linear Models

3

A minimum of 6 credits of electives drawn from courses offered by Mathematical Sciences, CSCI, and the School of Business Administration. These courses must be approved by the advisor.

6

A minimum of 2 research credits is required. A final presentation on a research project must be given in the Applied Math & Statistics seminar.

2

Total Hours

12-18

Minimum Required Grade: C