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Graduate Certificate 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 the contact Science Academy staff via email:


The Graduate Certificate in Data Science has a 12-credit, 4-course curriculum that provides a broad introduction to the field—including how to extract and clean data, store and manage large volumes of data, and analyze such data and extract insights from it. 

  • Provides an advanced introduction to the field and a strong foundation to develop future expertise. 
  • Data Science requires the ability to integrate data, operate on data at scale, analyze data, make predictions, find patterns and form and test hypothesis. It incorporates practices from a variety of fields in computer science, chiefly Machine Learning, Statistics, Databases, and Visualization.
  • Certificate program admission is only available to domestic students or students who do not need a visa to study.
  • Can be completed in nine months of continuous part-time enrollment. See Designation of Full-time/Part-time Status.


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 and Statistics
Core DATA602 Principles of Data Science
Core DATA604 Data Representation and Modeling
Elective DATA603 Principles of Machine Learning
Elective DATA605 Big Data Systems
Elective DATA606 Algorithms for Data Science

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 plan of study.

Sample Plan

Semester Year Course Number Section Code Credits
Fall 1 DATA601 PCS* 3
Fall 1 DATA602 PCS* 3
Spring 1 DATA604 PCS* 3
Spring 1 DATA60* PCS* 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 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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