Data Science

Engage in cutting-edge learning with the Master of Science in Data Science.

The MS in Data Science provides an education in the theory and practice of data science including mathematical and statistical foundations, computational approaches, and communication considerations. In addition, the program covers data science-relevant probability and statistics, algorithms, big data systems, machine learning, data mining, and analysis of networks. Students who successfully complete the MS in Data Science are able to design, conduct, interpret and communicate data analysis tasks and studies using methods and tools of statistics, machine learning, computer science, and communications. The program is offered through the Science Academy in the College of Computer, Mathematical, and Natural Sciences.

The MS in Data Science is a 30-credit graduate program designed for working professionals and can be completed in less than two years. Instruction is provided by UMD faculty and experts in the field. The program features instructional delivery through face-to-face instruction at the UMD College Park campus and at least one online course. The Science Academy has run a Master of Professional Studies (MPS) degree since Fall 2019. Current MPS students should follow the MS curriculum plan of study and contact the Science Academy with any advising questions or concerns.

Application Deadlines

Fall 2024
Domestic Students: June 14, 2024

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Admission Requirements

Any student applying for admission to a graduate program at the University of Maryland must meet the following minimum admission criteria as established by the Graduate School.

Applicants must have earned a four-year baccalaureate degree from a regionally accredited U.S. institution, or an equivalent degree from a non-U.S. institution.
Applicants must have earned a 3.0 GPA (on a 4.0 scale) in all prior undergraduate and graduate coursework.
Applicants must provide an official copy of a transcript for all of their post-secondary work.

General Requirements:

Statement of Purpose
Transcript(s)
TOEFL/IELTS/PTE (international graduate students)

Program-Specific Requirements:

Graduate Record Examination (GRE) (optional)
CV/Resume
Description of research/work experience
Prior coursework establishing quantitative ability (including calculus II, linear algebra, statistics, etc.)
Proficiency in programming languages, demonstrated either through prior programming coursework or substantial software development experience

Plan of Study & Courses

The MS in Data Science is a 30-credit, 10-course, non-thesis graduate program that culminates with research methods and study design. The program’s curriculum focuses on five thematic competencies as follows:

Statistics - Standard statistics subsumed by general linear models (e.g., linear regression, ANOVA, t-tests, f-tests, and multivariate extensions); discrimination, classification, ordination (e.g., PCA, MDS), linear discriminant analysis, factor analysis, and related methods; permutation and randomization methods; Bayesian estimation.
Machine learning - Methods that are not subsumed by general linear models or other traditional distributional model-based statistics. Includes such things as: support vector machines; artificial neural networks and their derivatives and extensions; decision tree induction; random forests; other ensemble methods; affinity analysis; association rule learning.
Computing - Topics include those core elements most necessary for professional practice in data science and analytics: databases; programming using scripting/interpretative languages (e.g., shell, Python, Perl).
Communication - Methods and practice of communicating data science and analytics concepts, methods and results in written, verbal, and electronic media.
Research/professional practice - Actual design, execution, and communication of a data science and analytics project.

Sample Plan of Study (Part-time, two 3-credit courses per semester)

Semester 1 (fall)	DATA601 Probability & Statistics (Core) DATA602 Principles of Data Science (Core)
Semester 2 (spring)	DATA603 Principles of Machine Learning (Core) DATA604 Data Representation and Modeling (Core)
Semester 3 (summer)	DATA612 Deep Learning (Elective) DATA641 Natural Language Processing (Elective)
Semester 4 (fall)	DATA607 Communication in Data Science and Analytics (Core) DATA650 Cloud Computing (Elective)
Semester 5 (spring)	DATA605 Big Data Systems (Core) DATA606 Algorithms for Data Science (Core)

Sample Plan of Study (Full-time, three 3-credit courses per semester)

Semester 1 (fall)	DATA601 Probability & Statistics (Core) DATA602 Principles of Data Science (Core) DATA603 Principles of Machine Learning (Core)
Semester 2 (spring)	DATA604 Data Representation and Modeling (Core) DATA605 Big Data Systems (Core) DATA606 Algorithms for Data Science (Core)
Semester 3 (summer)	DATA612 Deep Learning (Elective)
Semester 4 (fall)	DATA607 Communication in Data Science and Analytics (Core) DATA641 Natural Language Processing (Elective) DATA650 Cloud Computing (Elective)