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George Washington University Research Experience 

Location: Washington, DC
Required GPA: 3.0/4.0
Minimum TOEFL iBT:  80
Minimum IELTS: 6.0 (no individual band below 5.0)
Terms of Study: Summer 2016:
July 3rd to August 12th (6 weeks)
Application Deadlines: March 1st  
University Website: summer.gwu.edu/international-summer-programs

What George Washington University Research Experience has to Offer

  • Calling on students to “Bring Us Your Ambition,” this respected university located in the politically charged heart of America’s influential capital  is sure to inspire entrepreneurial and engaged international students.
  • The school is spread out across the Foggy Bottom neighborhood in downtown DC, near the State Department and about a mile away from museums on the National Mall and the Washington Monument.
  • George Washington is able to accommodate both undergraduate and graduate level participants. Students build their own schedule of classes according to their interests and in coordination with the Foundation. 


  • Students live on-campus in state-of the-art  housing located in the trendy neighborhoods of Foggy Bottom and Mount Vernon, just blocks away from the US Capital and within walking distance of Georgetown. Bathrooms and kitchens are shared. 


  • This unique research program is designed for students who wish to engage in an academic cross-disciplinary approach to applied research.
  • Program Structure:
    • Two courses: Introduction to Data Science, Writing and Communication for Data Science (see course description below)
    • Sessions on graduate admissions and the U.S. university experience
    • Site visits to research institutions in Washington, D.C.
  • The program is suitable for upper-level undergraduate and graduate students as they consider future careers and academic pathways in fields such as statistics, computer science, mathematics, bioinformatics, physics and the engineering sciences.
  • Students will receive 6 GWU credits upon completion of the program. 

Course Description:

  • Introduction to Data Science: This class covers the basic techniques of data science, algorithms for data mining, and introductory statistical modeling. Students learn to apply data science principles to disciplines from the natural sciences to social sciences that are characterized by the need to manage and analyze big data sets. Field professionals will provide real-world problems they are working to solve and students will work in groups to develop solutions. Typical areas of natural science include strophysics, bioinformatics, and mathematics. In social sciences, examples include economic forecasting, political campaign analytics, and geographic information systems (GIS). As a result of the course, students will gain skills in problem analysis, research, team work and communication.

  • Writing and Communication for Data Science: This course teaches students the principles and practices associated with academic writing in U.S. higher education and supports the development of a specialized skill set for the interdisciplinary field of data science. Students will learn to assess and respond to the communicative expectations of the field, receiving targeted instruction and feedback on a range of tasks that support their coursework in Data Science. Possible writing assignments include explaining concepts and data items, documenting client needs and project goals, proposing projects, and writing progress and research reports. Oral communication tasks may involve collaborative group projects, interviewing an expert in the field, and preparing and delivering presentations based on research. This course is taught by experienced faculty in the English for Academic Purposes (EAP) program, and the small class size offers many opportunities for students to interact with classmates and the professor in a supportive classroom environment.

Cost 2016: USD 10,000 

  • Includes tuition, administration and management fees, housing (double room in residence hall all meals included), airport pick up.
  • Field trips included.


  • Standing: Designed for upper-level undergraduate and graduate students considering future STEM careers or further academic pursuits. 
  • Suitable majors: statistics, computer science, mathematics, bioinformatics, physics and the engineering sciences.
  • Pre-requisite: Students should have taken courses in multivariate calculus and introductory statistics. Programming experience is desirable, ideally with Python. STEM courses are a plus.
  • Students should complete the SAF online application


Last Updated: 3/30/16