Master of Science in the Field of Data Analytics

Administered jointly through the Department of Computer Science and the Department of Engineering Management & Systems Engineering, the program seeks to address the growing demand for professionals skilled in big data and data analytics in government, industry and research organizations. Through courses led by top faculty members at SEAS and the GW School of Business, this program covers topics in computer science, business analytics, and systems engineering while focusing on the foundations of analytics from a technical, engineering perspective.

This program is ideal for those seeking to learn in a small, tight-knit cohort about the engineering foundations that propel the fields of data science and analytics; pursue or enhance careers as data analysts or data scientists; lead interdisciplinary teams; or apply data science and analytics techniques in the decision-making process of a wide range of organizations.


In addition to the entrance requirements, students are expected to be adequately prepared in calculus and probability/statistics concepts. The MS program requires students to have completed MATH 1231 and MATH 1232 (Calculus 1 and Calculus II), and APSC 3115 (Engineering Analysis III), or their equivalents. Background in linear algebra is strongly recommended but not required.

Educational Planner

In consultation with an academic advisor, each student must develop an Educational Planner through DegreeMAP that governs the student’s degree requirements. The Educational Planner should be established soon after matriculation and must be completed before the end of the student’s first semester. The Educational Planner must be approved by the advisor.

Credit Requirements

The program of study consists of graduate-level courses totaling 33 credits. Thesis is not a requirement for the completion of the program. The details of the required courses and electives are shown below.

CSCI 6362Probability for Computer Science
or EMSE 6765 Data Analysis for Engineers and Scientists
CSCI 6441Database Management Systems
or EMSE 6586 Data Management Systems for Data Analytics
CSCI 6444Introduction to Big Data and Analytics
EMSE 6574Programming for Analytics
SEAS 6401Data Analytics Capstone I
SEAS 6402Data Analytics Capstone II
Students take six elective courses, at least four of which must be in either the computer science track or in the engineering management and systems engineering track, effectively constituting a concentration in one of the two tracks. Up to two courses may be taken outside of SEAS; courses in the business analytics program are recommended.
Computer science track electives
CSCI 6212Design and Analysis of Algorithms
CSCI 6312Graph Theory and Applications
CSCI 6341Continuous Algorithms
CSCI 6342Computational Linear Algebra and Applications
CSCI 6351Data Compression
CSCI 6364Machine Learning
CSCI 6365Advanced Machine Learning
CSCI 6421Distributed and Cluster Computing
CSCI 6442Database Systems II
CSCI 6443Data Mining
CSCI 6451Information Retrieval Systems
CSCI 6515Natural Language Understanding
CSCI 6527Introduction to Computer Vision
Engineering management and systems engineering track electives
EMSE 6020Decision Making with Uncertainty
EMSE 6510Decision Support Systems and Models
EMSE 6575Applied Machine Learning for Analytics
EMSE 6579Applied Data Mining in Engineering Management
EMSE 6740Systems Thinking and Policy Modeling I
EMSE 6760Discrete Systems Simulation
EMSE 6770Techniques of Risk Analysis and Management

Graduation and Scholarship Requirements

Students are responsible for knowing the university’s minimum GPA requirement for graduation and scholarships.  Please visit the Graduation and Scholarship Requirements section on this site to read the requirements.

Students should contact the department for additional information and requirements.

Program Restrictions

Normally, only 6000 level courses (or higher) may be counted toward the requirements for the graduate degree.