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

The MPS in Data Science and Analytics 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.

Academic Planning Sheet for current students

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

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

Learn more about the courses

Find up to date tuition and fee information here for the MPS in Data Science and Analytics.