Master of Science in the Field of Data Analytics
Administered jointly through the Department of Computer Science and the Department of Engineering Management and 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 faculty members from SEAS and the GW School of Business, the program covers topics in computer science, business analytics, and systems engineering while focusing on the foundations of analytics from a technical, engineering perspective. Depending on their prior academic background, students must choose the computer science track or an engineering management and systems engineering track. All students are required to take a set of six core courses and three additional courses in their track.
The 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.
Prerequisites
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 following requirements must be fulfilled: 33 credits, including 18 credits in required courses and 15 credits in elective courses.
| Code | Title | Credits |
|---|---|---|
| Required | ||
| CSCI 6362 | Probability for Computer Science | |
| or EMSE 6765 | Data Analysis for Engineers and Scientists | |
| CSCI 6441 | Database Management Systems | |
| or EMSE 6586 | Data Management Systems for Data Analytics | |
| CSCI 6444 | Introduction to Big Data and Analytics | |
| EMSE 6574 | Programming for Analytics | |
| SEAS 6401 | Data Analytics Foundations and Practicum | |
| SEAS 6402 | Data Analytics Capstone | |
| Additional coursework | ||
| Five additional courses are required. At least three of these courses (two required and one elective) 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. With the advisor’s approval, the remaining elective course may be taken outside of the selected track and may include courses outside SEAS. | ||
| Computer science track | ||
| If the computer science track is selected, students must take CSCI 6212 and CSCI 6364 and one elective course from the list below. | ||
| Required | ||
| CSCI 6212 | Design and Analysis of Algorithms | |
| CSCI 6364 | Machine Learning | |
| Electives | ||
| CSCI 6312 | Graph Theory and Applications | |
| CSCI 6341 | Continuous Algorithms | |
| CSCI 6342 | Computational Linear Algebra and Applications | |
| CSCI 6351 | Data Compression | |
| CSCI 6365 | Advanced Machine Learning | |
| CSCI 6421 | Distributed and Cluster Computing | |
| CSCI 6442 | Database Systems II | |
| CSCI 6443 | Data Mining | |
| CSCI 6451 | Information Retrieval Systems | |
| CSCI 6515 | Natural Language Understanding | |
| CSCI 6527 | Introduction to Computer Vision | |
| Engineering management and systems engineering track | ||
| If the engineering management and systems engineering track is selected, students must take EMSE 6575 and EMSE 6577and one elective course from the list below. | ||
| Required | ||
| EMSE 6575 | Applied Machine Learning for Analytics | |
| EMSE 6577 | Data-Driven Policy | |
| Electives | ||
| EMSE 6020 | Decision Making with Uncertainty | |
| EMSE 6035 | Marketing of Technology | |
| EMSE 6510 | Decision Support Systems and Models | |
| EMSE 6579 | Applied Data Mining in Engineering Management | |
| EMSE 6740 | Systems Thinking and Policy Modeling I | |
| EMSE 6760 | Discrete Systems Simulation | |
| EMSE 6770 | Techniques 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.