Master of Professional Studies in Data Science and Analytics
Winter Term begins November 25, 2019. Domestic students should apply by October 18, 2019.
Fall Term begins August 31, 2020. The registration deadline for international students is March 13, 2020. Domestic students should apply by July 1, 2020 for best consideration. The registration deadline for domestic students is July 31, 2020.
About the Program
Engage in cutting-edge learning with the Master of Professional Studies in Data Science and Analytics.
The MPS in Data Science and Analytics 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 MPS in Data Science and Analytics should be 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 MPS in Data Science and Analytics is a 30-credit graduate program designed to accommodate working professionals while minimally disrupting personal and professional life. Instruction is provided by UMD faculty and experts in the field. The program features instructional delivery through online, blended, and face-to-face modalities. Face to face classes meet at the UMD College Park campus; online and blended classes are offered through UMD’s online learning environment which delivers content through easy to use web-based technology, enabling learning in an engaging, interactive environment. The MPS in Data Science and Analytics uses the term academic calendar: in a 12-week term, students complete course work in 11 weeks; week 12 is reserved for finals.
Plan of Study & Courses
The MPS in Data Science and Analytics is a 30-credit, 10-course, non-thesis graduate program which culminates with research methods, study design, and a capstone project. 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.
Suggested Plan of Study (full-time; two 3-credit courses per term):
Term 1: DATA601 Probability & Statistics (Core)
DATA602 Principles of Data Science (Core)
Term 2: DATA603 Principles of Machine Learning (Core)
DATA604 Data Representation and Modeling (Core)
Term 3: DATA605 Big Data Systems (Core)
DATA606 Algorithms for Data Science (Core)
Term 4: DATA607 Communication in Data Science and Analytics Methods (Core)
DATA698 Research Methods and Study Design (Methods) (can be taken simultaneously with other course except DATA699)
Term 5: DATA612 Deep Learning (Elective) (prereq: DATA603)
DATA699 Capstone Research Project (prereq: DATA698)
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.
Statement of Purpose
TOEFL/IELTS/PTE (international graduate students)
Graduate Record Examination (GRE) (optional)
Description of research/work experience
Prior coursework establishing quantitative ability (i.e. calculus, linear algebra, basic statistics, etc.)
Proficiency in programming languages, demonstrated either through prior programming coursework or substantial software development experience
Tuition and Fees