Office of Graduate Admissions & Student Services

School of Engineering and Applied Science

Science & Engineering Hall

800 22nd Street, NW n Suite 2885

Washington, D.C. 20052

engineering@gwu.edu | 202-994-1802

http://graduate.seas.gwu.edu

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@gwgradengineer

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The George Washington University does not unlawfully discriminate in its admissions programs against any person based on that person’s race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, or gender identity or expression. SEAS-1617-10

CONTACT Information

HOW TO APPLY

CAREER RESOURCES

SOCIAL MEDIA Links

ADMISSION REQUIREMENTS

• Recommended 3.0 GPA (out

of a 4.0 scale) in the last 60

credit hours of undergraduate

coursework

• GRE scores

• Two letters of recommendation

• Academic Records (e.g.,

transcripts, marksheets)

• Statement of Purpose

• Résumé/CV

• International applicants:

Scores from the TOEFL,

IELTS, or PTE Academic

exams

W. SCOTT AMEY CAREER SERVICES CENTER At SEAS, we want your degree to matter. Our career services team is

here to help you find a job or internship by providing the resources

to improve your résumé, build skills in networking and interviewing,

and sharpen your job search techniques. In addition, we organize

workshops, one-on-one coaching sessions, and employer visits to

help you find the right career for you, anywhere in the world.

washington, d.c.

careers.seas.gwu.edu

seascareers@gwu.edu

@GWSEASCareers

Master’s Program in Data Analytics

DEADLINES

FALL—January 15

SPRING—September 1

SUMMER—March 1 (U.S. citizens only)

Better in

APPLY NOW:

http://go.gwu.edu/applytoseas

WHY DATA ANALYTICS?

Big data is increasingly being used to track, analyze, and inform major decisions for policy, business, and other organizational

needs. According to a study by the McKinsey Global Institute, the U.S. will soon face a shortage of data scientists, managers, and

analysts who can understand and make decisions using big data.

“Big data will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus–as long as the right

policies and enablers are in place.” –McKinsey Global Institute

As the demand for professionals well-versed in data interpretation grows, the opportunities to start a career in big data can begin

with the right education that covers the fundamentals of computer science and data analysis, and enhanced by engineering

principles and data management. Data is no longer limited to tech companies and statistical organizations–it can be applied to

virtually any sector, making a well-rounded education in data analytics essential to one’s professional success.

M.S. IN DATA ANALYTICS AT GW

Administered jointly through the Department of Computer Science and the Department of Engineering Management and Systems

Engineering, the Master of Science in Data Analytics program at GW addresses the growing demand for professionals skilled in

big data and data analytics. It provides a comprehensive and rigorous curriculum that covers all aspects of data acquisition and

management, allowing for students to apply their skills toward any sector.

GW and its main campus in Washington, DC, is ideally situated for students to take advantage of the endless opportunities to

apply data analytics skills in the real world. In addition to serving as a world policymaking center, the DC metro area is home to an

increasing number of technology firms and corporations, which actively seek graduates with data skills.

The M.S. in Data Analytics at GW integrates education with cutting edge research supported by top research faculty at the School

of Engineering and Applied Science. Students can choose between two tracks to focus their degree on either Computer Science or

Engineering Management & Systems Engineering. Students who complete this program will be intellectually prepared for a wide

range of data analytics jobs, or to pursue a doctorate degree afterward, if they choose to.

Students can expect to study in small cohorts of 20-30 students and conclude their time at GW through a group capstone project.

This allows students to apply what they have learned in a real-world setting, developing teamwork and research skills.

FACULTY RESEARCH HIGHLIGHT

David Broniatowski, Department of Engineering Management and Systems Engineering

David Broniatowski is an assistant professor and director of the Decision Making and

Systems Architecture Laboratory where he conducts research in decision making under

risk, group decision making, system architecture, and behavioral epidemiology. His

current projects include text network analysis of group decision-making in government,

using social media data to track health outcomes, formal models of decision under risk,

and mathematical theories of system architecture.

“Our program allows you to apply the technical skills if you have them, to develop

the technical skills if you don’t, and to interface with the business community if you

want to.”

PROGRAM OUTCOMES & EXPECTED JOB TITLES

QUICK FACTS:

Total credit hours: 33

Program Duration: Two years (full-time)

Core Courses (15 credits)

• Introduction to Big Data

and Analytics

• Programming for Analytics

• Data Management

• Probability and Statistics

• Capstone project

TRACK 1

COMPUTER SCIENCE (12 CREDITS)

Sample courses:

• Machine Learning

• Data Mining

• Cloud Computing

• Natural Language Processing

TRACK 2

ENGINEERING MANAGEMENT &

SYSTEMS ENGINEERING (12 CREDITS)

Sample courses:

• Data Mining and Processing

• Data Driven Decision Making and Policy

• Decision Support Systems

• Risk Analysis and Management

Upon completion of the program, graduates of the M.S. in Data Analytics program can expect to:

• Apply different techniques and types of analytics to

solve real-world problems in a variety of technical and

business organizations

• Demonstrate the ability to store, clean, and transform

large-scale data sets

• Improve decision-making processes in organizations by

applying analytics techniques to interpret data

Expected Job titles:

• Data Analyst

• Data Engineer

• Analytics Manager

• Data Security Analyst

• Data Scientist

*Remaining 6 credit hours are elective courses students may select from any department with advisor approval.