M.S. Degree in Data Science
The Master's of Science degree program in Data Science is an interdisciplinary program hosted by the Department of Computer Science and the Department of Mathematical Sciences. It situates computing-specific competencies in computer science and statistical related fields including database, data mining, machine learning, and big data within the broader interdisciplinary space. It uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structured and unstructured data. One may think of data science as a blending together of methods and ideas from analysis, statistics, databases, big data, artificial intelligence, numerical analysis, graph theory, and visualization for the purposes of finding information in data and applying that information to solving real-world problems. It incorporates empirical, theoretical, computational techniques to solidify data-driven decisions. The program offers a thesis track as well as a non-thesis track. The program also offers opportunities to the students to extend the interdisciplinary space to other disciplines beyond Math and CS.
The key competencies for the Master's degree in Data Science include:
- Computational and statistical thinking, and applied mathematical foundations
- Algorithms and software foundations
- Computing fundamentals, including programming, data structures, algorithms, and software engineering
- Data acquirement and governance
- Data management, storage, and retrieval
- Data mining and machine learning
- Big data, including complexity, distributed systems, parallel computing, and high-performance computing
- Analysis and presentation, including human-computer interaction and visualization
- Model building and assessment
The formal program requirements are listed in the University Catalog. Students entering the Master's program in Data Science are expected to have a Bachelor's Degree from an accredited college or university with a grade point average of 3.0 out of 4.0 and with the following mathematics and computer science courses completed before beginning the program. If all the courses have not been completed by the time of application, then acceptance into the program will be conditional upon the remaining courses being completed before beginning the program.
- Math 21001 - Linear Algebra or Math 21002 - Applied Linear Algebra or their equivalent
- Math 20011 - Decision-Making Under Uncertainty or its equivalent
- Math 22005 - Analytic Geometry and Calculus III or its equivalent
- Math 23022 - Discrete Structures for Computer Science or Math 31011 - Proofs in Discrete Mathematics or their equivalent
- CS 23001 - Computer Science II: Data Structures and Abstraction; or its equivalent
- CS 33007 - Introduction to Database System Design or its equivalent
In addition to standard university requirements, acceptance into the program requires a cumulative GPA of 3.0 for the prerequisite courses, GRE scores, and, for students whose native language is not English, proof of English proficiency. Proof of English proficiency may be a transcript showing full-time attendance for at least one academic year at a university where the instruction is in English, or TOEFL scores of 71 on the IBT or 525 on the PBT. IELTS scores may be submitted in place of the TOEFL. The minimum IELTS requirement is a score of 6.0.
What to Submit?
- Official transcript(s)
- Two letters of recommendations
- GRE Scores
- Proof of English language proficiency
Where to Submit?
We accept applications year-round and review them as they come in. It is important to complete and submit your application as early as possible. It can sometimes take several weeks to route and process your application.
The formal program requirements are listed in the University Catalog, but in summary, students must complete 30 credit hours of which 18 credit hours are required courses, 6 credit hours of electives, and 6 credit hours of culminating experience.
Required Courses (18 Credit Hours)
- Math 50015: Applied Statistics (3) (Fall)
- Math 50024: Computational Statistic (3) (Spring)
- Math 50028: Statistical Learning (3) (Spring)
- CS 63005: Advanced Database Systems Design (3) (Fall)
- CS 63015: Data Mining Techniques (3) (Spring)
- CS 63016: Big Data Analytics (3) (Spring)
Elective Courses (6 Credit Hours)
- Math: 50011 Probability Theory and Applications (3)
- Math: 50051 Topics in Probability and Stochastic Processes (3)
- Math: 50059 Stochastic Actuarial Models (3)
- Math: 67098 or Math 67098 Research (3)
- CS 54201 Artificial Intelligence (3)
- CS 57206 Data Security and Privacy (3)
- CS 63017 Big Data Management (3)
- CS 63018 Probabilistic Data Management (3)
- CS 63100 Computational Health Informatics (3)
- CS 64201 Advanced Artificial Intelligence (3)
- CS 64402 Multimedia Systems and Biometrics (3)
- CS 67302 Information Visualization (3)
- BSCI 60104 Biological Statistics (3)
- GEOG 59070 Geographic Information Science (3)
- GEOG 59072: Geographic Information Science and Health (3)
- GEOG 59075: GIS Applications for Social Problems (3)
- GEOG 59078: GIS and Environmental Hazards (3)
- GEOG 59080: Advanced Geographic Information Science (3)
- PSYC 61651 Quantitative Statistical Analysis I (3)
- PSYC 61641 Quantitative Statistical Analysis II (3)
- ECON 62054 Econometrics I (3)
- ECON 62055 Econometrics II (3)
- ECON 62056 Time Series Analysis (3)
- EHS 52018 Environmental Health Concepts in Public Health (3)
- EHS 53009: Emerging Environmental Health Issues and Responses (3)
- EPI 52017 Fundamentals of Public Health Epidemiology (3)
- EPI 52016 Principles of Epidemiological Research (3)
- EPI 63018 Observational Designs for Clinical Research (3)
- EPI 63019 Experimental Designs for Clinical Research (3)
- HI 60401 Health Information Management (3)
- HI 60411 Clinical Analytics (3)
- HI 60414 Human Factors and Usability in Health Informatics (3)
- HI 60418 Clinical Analytics II (3)
- KM 60301 Foundational Principles of Knowledge Management (3)
- LIS 60010 The Information Landscape (3)
- LIS 60636 Knowledge Organization Structures, Systems and Services (3)
- LIS 60637 Metadata Architecture and Implementation (3)
- LIS 60638 Digital Libraries (3)
Culminating Experience (6 Credit Hours)
- CS 69099 Capstone Project (6)
- CS 69099 Capstone Project (3) and CS 69192 Graduate Internship (3)
- CS 69119: Thesis I (6)
Plan of Work
|Course Work||Credit Hours (30)|
|First Semester (Fall)|
|Math 50015: Applied Statistics||3|
|CS 63005: Advanced Database Systems Design||3|
|Second Semester (Spring)|
|Math 50024: Computational Statistic||3|
|Math 50028: Statistical Learning||3|
|CS 63015: Data Mining Techniques||3|
|Third Semester (Fall)|
|Fourth Semester (Spring)|
|CS 63016: Big Data Analytics||3|
|Capstone, or Capstone + Internship, or Thesis I||6|
- Successful completion of 30 credit hours.
- Successful completion of the Capstone Project for the non-thesis option.
- Successful thesis defense for the thesis option.
- Six of the elective credit hours must be taken at the 60000 levels.
- GPA must be kept greater than or equal to 3.0 all the time.