International Deadline : February 26, 2027 Domestic Deadline : May 28, 2027 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.