Master of Data Science
Collaborative program with the Department of Applied Mathematics
This professional master’s degree program consists of 33 credit hours of coursework including a six credit hour practicum project. The program is designed primarily for those with previous degrees or experience in computer science, statistics, mathematics, the natural or social sciences, or business, and who are interested in preparing for a career as a data science professional in business and industry. Full-time students may complete the program in one year, including one summer term.
Admission Requirements
Applicants should have a bachelor’s degree from an accredited university with a minimum cumulative GPA of 3.0/4.0. A combined verbal and quantitative GRE examination score of at least 304 and an analytical writing score of at least 3.0 are required. The GRE requirement may be waived for students with a bachelor’s degree from an accredited college or university in the United States with a cumulative GPA of at least 3.0/4.0.
Prerequisites include knowledge of a high level programming language at the level of CS 201 (object-oriented programming is required), a data structures and algorithms course at the level of CS 331, multivariate calculus at the level of MATH 251, linear algebra at the level of MATH 332, and probability and statistics at the level of MATH 474. Information on these courses is available in this bulletin. Proficiency and placement exams are also available.
Students with an insufficient background in computer science and/or mathematics will be required to take the relevant prerequisite courses and earn at least a "B" grade in each. These prerequisite courses do not count toward the 33 credit hour requirement.
Curriculum
Coursework includes 18 credit hours of required core courses, nine credit hours of elective courses, and six credit hours of CSP 572 Data Science Practicum. At least nine credit hours must be taken of 400- or 500-level CS or CSP courses and nine credit hours of 400- or 500-level MATH courses, not including the CSP 572 Data Science Practicum.
Up to six credit hours of 400-level undergraduate coursework may be used toward degree requirements.
| Code | Title | Credit Hours |
|---|---|---|
| Data Science Core Courses | (15) | |
| MATH 563 | Mathematical Statistics | 3 |
| or MATH 564 | Applied Statistics | |
| CS 584 | Machine Learning | 3 |
| or MATH 569 | Statistical Learning | |
| SCI 511 | Project Management | 3 |
| or SCI 522 | Public Engagement Scientists | |
| CSP 571 | Data Preparation and Analysis | 3 |
| Select a minimum of one course from the following: | 3 | |
| Advanced Database Organization | 3 | |
| Data-Intensive Computing | 3 | |
| Big Data Technologies | 3 | |
| Data Science Capstone | (6) | |
| CSP 572 | Data Science Practicum | 6 |
| Data Science Electives | (12) | |
| Select 9 to 12 credit hours of Data Science Electives | 12 | |
| Total Credit Hours | 33 | |
Data Science Electives
| Code | Title | Credit Hours |
|---|---|---|
| Computational Fundamentals | ||
| Database Organization | 3 | |
| Introduction to Algorithms | 3 | |
| Operating Systems | 3 | |
| Data Integration Warehousing | 3 | |
| Advanced Database Organization | 3 | |
| Data Privacy and Security | 3 | |
| Dsgn and Anlys of Algorithms | 3 | |
| Parallel and Distributed Proc | 3 | |
| Cloud Computing | 3 | |
| Data-Intensive Computing | 3 | |
| Software Testing and Anlys | 3 | |
| Big Data Technologies | 3 | |
| Computer Science Applications | ||
| Data Mining | 3 | |
| Computer Vision | 3 | |
| Geospatial Vision/Visualizatio | 3 | |
| Advanced Data Mining | 3 | |
| Information Retrieval | 3 | |
| Cyber-Physical Sys: Lang & Sys | 3 | |
| Cyber-Physical Sys Sec/Dsgn | 3 | |
| Deep Learning | 3 | |
| Interact/Trans Mach Learning | 3 | |
| Online Social Network Analysis | 3 | |
| Advanced Artificial Intelligen | 3 | |
| Probabilistic Graphical Models | 3 | |
| Machine Learning | 3 | |
| Natural Language Processing | 3 | |
| Mathematics, Probability, and Statistics | ||
| Graph Theory and Applications | 3 | |
| Intro to Stochastic Processes | 3 | |
| Design and Analysis of Exprmnt | 3 | |
| Mathematical Modeling I | 3 | |
| Mathematical Modeling II | 3 | |
| Mathematical Modeling | 3 | |
| Linear Algebra | 3 | |
| Optimization I | 3 | |
| Probability | 3 | |
| Stochastic Processes | 3 | |
| Introduction to Time Series | 3 | |
| Monte Carlo Methods in Fin | 3 | |
| Multivariate Analysis | 3 | |
| Adv Design of Experiments | 3 | |
| Statistical Learning | 3 | |
| Machine Learning in Finance: | 3 | |
| Bayesian Computational Stats | 3 | |
| Mathematical and Scientific Computing | ||
| Bioinformatics | 3 | |
| Computational Mathematics I | 3 | |
| Computational Mathematics II | 3 | |
| Meshfree Methods | 3 | |
| Computational Physics | 3 | |
| Professional Skills | ||
| Project Management | 3 | |
| Public Engagement Scientists | 3 | |
| Fundamentals of Design | 3 | |
| User Experience Research/Eval | 3 | |
Master of Data Science Curriculum
| Year 1 | |||||
|---|---|---|---|---|---|
| Semester 1 | Credit Hours | Semester 2 | Credit Hours | Semester 3 | Credit Hours |
| CS 525, 554, or CSP 554 | 3 | CS 584 or MATH 569 | 3 | CSP 572 | 6 |
| MATH 563 or 564 | 3 | CSP 571 | 3 | ||
| SCI 511 or CS 587 | 3 | Data Science Elective | 3 | ||
| 9 | 9 | 6 | |||
| Year 2 | |||||
| Semester 1 | Credit Hours | ||||
| SCI 522 | 3 | ||||
| Data Science Elective | 3 | ||||
| Data Science Elective | 3 | ||||
| 9 | |||||
| Total Credit Hours: 33 | |||||
