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Master of Science in Data Science

The Science Academy, housed in the College of Computer, Mathematical, and Natural Sciences (CMNS), draws on the university’s collective expertise to provide academic programs that are both rigorous and relevant. Science Academy Graduate Programs translate research into applied knowledge and provides current and future professionals with invaluable skills.

Mentoring and advising are an essential part of the program. Students meet with faculty and the academic program director to ensure that educational goals and career learning and development goals are met. Students should contact Science Academy staff via email:


The Master of Science in Data Science is a 30-credit, 10-course, non-thesis graduate program that culminates with research methods, study design, and a capstone project.

  • Digital data is being generated at such a rapid pace, resulting in ‘Big Data’ and requiring new techniques for processing and analysis. Whether it’s called big data analytics, predictive analytics, or advanced analytics, Data Science is a growing, evolving field—and nearly every industry is in need of professionals who have the skills required to guide decision-making processes.
  • Get a degree that provides the skills required to compete in this exciting career.
  • Provides an education in the theory and practice of data science including mathematical and statistical foundations, computational approaches, and communication considerations.
  • Covers data science-relevant probability and statistics, algorithms, big data systems, machine learning, data mining, and analysis of networks.
  • Successful graduates 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.
  • Can be completed in sixteen months of continuous full-time enrollment. Part-time enrollment is welcome. See Designation of Full-time/Part-time Status.

Program Features

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.


Below is a listing of all program courses. For a detailed course description that includes pre-requisites or co-requisites, see The Graduate School Catalog, Course Listing as follows: DATA Course Descriptions.

Type Course Number Title
Core DATA601 Probability & Statistics
Core DATA602 Principles of Data Science
Core DATA603 Principles of Machine Learning
Core DATA604 Data Representation and Modeling
Core DATA605 Big Data Systems
Core DATA606 Algorithms for Data Science
Core DATA607 Communication in Data Science and Analytics
Methods DATA698 Research Methods and Study Design
Elective DATA612 Deep Learning

Registration Overview

  • See the sample plan of study, below. Students should use this as a guide to develop a plan with the academic program director. 
  • Actual course offerings are determined by the program and may vary semester to semester. Students should note if a course has a pre-requisite or co-requisite. 
  • Specific class meeting information (days and time) is posted on UMD’s interactive web service services, Testudo. Once on that site, select “Schedule of Classes,” then the term/year. Courses are listed by academic unit. 
  • The program uses specific section codes for registration, which are listed on the sample of study.

Sample Plan, Full-Time 

Semester Year Course Number Section Code Credits
Fall 1 DATA601 PCS* 3
Fall 1 DATA602 PCS* 3
Fall 1 DATA603 PCS* 3
Spring 1 DATA604 PCS* 3
Spring 1 DATA605 PCS* 3
Spring 1 DATA606 PWS* 3
Summer 1 DATA612 PWS* 3
Fall 2 DATA641 PCS* 3
Fall 2 DATA607 PCS* 3
Fall 2 DATA650 PCS* 3

Sample Plan, Part-Time

Semester Year Course Number Section Code Credits
Fall 1 DATA601 PCS* 3
Fall 1 DATA602 PCS* 3
Spring 1 DATA604 PCS* 3
Spring 1 DATA603 PCS* 3
Summer 1 DATA612 or DATA698 PCS* 3
Fall 2 DATA650 PCS* 3
Fall 2 DATA607 PCS* 3
Spring 2 DATA605 PCS* 3
Spring 2 DATA641 PCS* 3
Summer 2 DATA606 PWS* 3


  • Uses the semester academic calendar with classes held in the fall and spring semester (16 weeks each).
  • Instructors present dynamic and interactive seminar-style instruction. 
  • Instruction provided by University of Maryland faculty and professionals in the field.

In-Person Learning

  • Classes meet in UMD College Park campus classrooms, offering a focused, distraction-free learning environment.
  • Classes are held weekday evenings (e.g., after 5:00 p.m.) to accommodate the working professional’s schedule
  • Students enrolled in a program that features in-person instruction are required to submit the University’s Immunization Record Form prior to the first day of their first semester/term. See Health Requirements.
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