Students can complete the ELC requirement in a variety of ways:

1. Participate in a Brown University credit experience (i.e. independent study):
   1. Complete a one-semester independent study for credit that focuses on research in data science or applies data-science techniques to a problem in the student’s concentration (e.g. as part of the student’s honors thesis, though this is not required).​
   2. Be a Data Science Fellow. Students may complete DATA 1150 and the concurrent Data Science Fellows project to fulfill both the elective and experiential components of the certificate.
2. Participate in a non-credit experience such as:
   1. Completing a data-related summer internship
   2. TA-ing for a data-related course (TA'ing for any of the DATA courses or courses on the certificate's elective list fulfills this requirement)
   3. Working with a local organization on a data-related project. 

Students who choose a non-credit option will be required to complete a reflective essay on their experience. This essay is due in your last semester (last day of November for December graudates and last day of April for May graduates). Read more about the requirements for this reflective essay below. 

• Students are responsible for identifying an appropriate experiential learning  component.
• Students must submit their proposal for their experiential component by the end of their sixth semester.
• Students should normally complete the experiential learning component during the summer following their junior year or during their senior year. The experiential learning component must be approved in advance by the student’s certificate advisor. Students will be asked to prepare a detailed proposal that explains how the experiential component draws on data science and how it relates to their concentration.
• Students who choose to pursue a no-credit option for the experiential learning component requirement must write and submit a 10-12 page paper during their senior year that reflects on the nature of the work done, its relationship to the learning goals of the data fluency certificate program, and its relevance for the student’s concentration. The final reflection should address the following aspects:
  • Describe how the experiential component you undertook is situated in a larger process of producing or disseminating knowledge, how it has drawn on your certificate coursework, and how it relates to your concentration.
  • How would you define the primary objectives of the project that you were involved with? Did some of these objectives compete with each other, and if so how? Describe the data-science aspects of your project and the potential impact your work has on individuals or society at large.
• The report should be submitted online as an uploaded file under the heading "Non-credit Capstone Option" in the Data Fluency Certificate declaration on ASK.

Coming soon! 

Have a question about the Experiential Learning Component that wasn't addressed above? Please review our Certificate FAQ page and reach out to dsi-ug-certificate@brown.edu if you still have questions!