Data Science (MS)
Students earning a MS in Data Science will gain a wide variety of skills needed to work with many different types of data, and to analyze, visualize, and extract useful information from data in a variety of ways. They will apply those skills in various contexts, especially during their capstone consulting class or thesis work. This 30-credit program has two tracks (thesis and non-thesis), can be completed full-time or part-time, and includes courses from Computer Science, Statistics, and Mathematics.
CURRICULUM
The Master of Science in Data Science requires 30 hours of coursework and a capstone project. Students may elect to conduct research and write a thesis instead of the capstone.
Non-Thesis Track
| Code | Title | Hours |
|---|---|---|
| Statistics Requirements | 6 | |
| Applied Regression Analysis | ||
| Categorical Data Analysis | ||
| Computer Science Requirements | 6 | |
| Database Programming | ||
| Big Data Analytics | ||
| Data Science Core | 9 | |
| Introduction to Data Science | ||
| Introduction to Predictive Analytics | ||
or COMP 479 | Machine Learning | |
| Data Science Consulting (Capstone) | ||
| Three Statistics or Computer Science 400 Level Electives 1,2 | 9 | |
| Total Hours | 30 | |
- 1
A list of electives can be found later in this document.
- 2
For electives, students must take one COMP class for 3 credit hours, one STAT class for 3 credit hours, and one course in either COMP or STAT for 3 credit hours.
Thesis Track
| Code | Title | Hours |
|---|---|---|
| Statistics Requirements | 6 | |
| Applied Regression Analysis | ||
| Categorical Data Analysis | ||
| Computer Science Requirements | 6 | |
| Database Programming | ||
| Big Data Analytics | ||
| Data Science Core | 7 | |
| Introduction to Data Science | ||
| Introduction to Predictive Analytics | ||
or COMP 479 | Machine Learning | |
| Research | 11 | |
| Data Science Research Design | ||
| Data Science Research | ||
| Thesis Supervision | ||
| Total Hours | 30 | |
Electives
The list of electives is divided into primary and secondary electives. Primary electives are recommended classes to all data science students. Secondary electives are courses that may be a good fit for students with a specific area of emphasis. Students need to coordinate with the Graduate Program Director if they plan to take a secondary elective.
| Code | Title | Hours |
|---|---|---|
| Primary COMP electives | ||
| COMP 406 | Data Mining | 3 |
| COMP 429 | Natural Language Processing | 3 |
| COMP 484 | Artificial Intelligence | 3 |
| COMP 487 | Deep Learning | 3 |
| COMP 488 | Computer Science Topics (If the topic is relevant to data science. Example: Topics in Computer Vision) | 1-4 |
| Primary STAT electives | ||
| STAT 411 | Applied Survival Analysis | 3 |
| STAT 421 | Math Modeling & Simulation | 3 |
| or COMP 421 | Math Models & Simulation | |
| STAT 451 | Applied Nonparametric Methods | 3 |
| STAT 444 | Longitudinal Data Analysis and Mixed Modeling | 3 |
| STAT 488 | Topics in Statistics (If the topic is relevant to data science. Examples: Multivariate Statistics, Bayesian Statistics) | 1-3 |
| Secondary COMP electives | ||
| COMP 436 | Markup Languages | 3 |
| COMP 441 | Human-Computer Interaction | 3 |
| COMP 460 | Algorithms & Complexity | 3 |
| COMP 405 | Database Administration | 3 |
| COMP 412 | Open Source Computing | 3 |
| COMP 413 | Intermediate Object-Oriented Development | 3 |
| COMP 418 | Combinatorial Mathematics | 3 |
| COMP 424 | Client-Side Web Design | 3 |
| COMP 474 | Software Engineering | 3 |
| COMP 422 | Software Development for Wireless and Mobile Devices | 3 |
| COMP 417 | Social and Ethical Issues in Computing | 3 |
| COMP 490 | Independent Project | 1-6 |
| COMP 499 | Internship | 1-6 |
| COMP 477 | IT Project Management | 3 |
| Secondary STAT electives | ||
| STAT 403 | SAS Program & Applied Statistics | 3 |
| STAT 407 | Statistical Design | 3 |
| STAT 404 | Probability & Statistics I | 3 |
| STAT 405 | Probability & Statistics II | 3 |
| STAT 498 | Independent Study Statistics | 1-6 |
Suggested Sequence of Courses
The below sequence of courses is meant to be used as a suggested path for completing coursework. An individual student’s completion of requirements depends on course offerings in a given term as well as the start term for a major or graduate study. Students should consult their advisor for assistance with course selection.
Non-thesis Track
| Year One | ||
|---|---|---|
| Fall | Hours | |
| DSCI 401 | Introduction to Data Science | 4 |
| STAT 408 | Applied Regression Analysis | 3 |
| COMP 453 | Database Programming | 3 |
| Hours | 10 | |
| Spring | ||
| STAT 410 | Categorical Data Analysis | 3 |
| COMP 458 | Big Data Analytics | 3 |
| COMP or STAT 400-Level Elective 1 | 3 | |
| Hours | 9 | |
| Year Two | ||
| Fall | ||
| STAT 438 or COMP 479 |
Introduction to Predictive Analytics or Machine Learning |
3 |
| COMP or STAT 400-Level Elective 1 | 3 | |
| COMP or STAT 400-Level Elective 1 | 3 | |
| DSCI 470 | Data Science Consulting | 2 |
| Hours | 11 | |
| Total Hours | 30 | |
- 1
For electives, students must take one COMP class for 3 credit hours, one STAT class for 3 credit hours, and one course in either COMP or STAT for 3 credit hours.
Thesis Track
| Year One | ||
|---|---|---|
| Fall | Hours | |
| DSCI 401 | Introduction to Data Science | 4 |
| STAT 408 | Applied Regression Analysis | 3 |
| DSCI 494 | Data Science Research Design | 2 |
| Hours | 9 | |
| Spring | ||
| STAT 410 | Categorical Data Analysis | 3 |
| DSCI 499 | Data Science Research | 3 |
| COMP 458 | Big Data Analytics | 3 |
| Hours | 9 | |
| Year Two | ||
| Fall | ||
| STAT 438 or COMP 479 |
Introduction to Predictive Analytics or Machine Learning |
3 |
| DSCI 499 | Data Science Research | 3 |
| COMP 453 | Database Programming | 3 |
| Hours | 9 | |
| Spring | ||
| DSCI 499 | Data Science Research | 2 |
| DSCI 595 | Thesis Supervision | 1 |
| Hours | 3 | |
| Total Hours | 30 | |
Responsible Conduct of Research
All PhD students and students in thesis-based Master's degree programs must successfully complete UNIV 370 Responsible Conduct in Research and Scholarship or other approved coursework in responsible conduct of research as part of the degree requirements. It is strongly recommended that students complete this two-day training before beginning the dissertation/thesis stage of the program.
Graduate & Professional Standards and Regulations
Students in graduate and professional programs can find their Academic Policies in Graduate and Professional Academic Standards and Regulations under their school. Any additional University Policies supersede school policies.
LEARNING OUTCOMES
- The ability to manage large data sets in preparation for data science analysis.
- A working knowledge of statistical techniques and computer algorithms, and the ability to apply these methods to a wide array of real-world problems.
- The ability to perform a data science analysis from beginning to end while adhering to the principles of reproducible and ethical research.
- The ability to program in both the R and Python programming languages.
- Complete a project demonstrating competence in the field of data science. Non-thesis track: Students will be required to complete a real-world data science project prior to graduating from this program, either through our consulting course, an internship, an independent study, or other appropriate project. Thesis track: Students will be required to undertake a research project culminating in a thesis.