Data Science Institute

Certificate in Data Fluency

The Certificate in Data Fluency is for Brown undergraduate students who wish to gain fluency and facility with the tools of data analysis and its conceptual framework, but who are not pursuing a concentration in a data-intensive discipline.

The program is designed to provide fundamental conceptual knowledge and technical skills to students with a range of intellectual backgrounds and concentrations, while emphasizing a critical liberal learning perspective. 

Data Fluency Certificate in the University Bulletin


Data fluency implies a familiarity with data science and a basic competency working with data. Many disciplines now require an understanding of how data are collected, stored, analyzed, and visualized. The purpose of this certificate is to prepare undergraduate students for a world that is increasingly data-centric. A massive shift has occurred in our basic interactions with one another, our understanding of society and culture, and our modes of inquiry and investigation. The growth of methods for exploring and analyzing data combined with advances in hardware capability has created possibilities for data analysis on a scale previously unimagined. At the same time, the societal impact of decisions based on data poses many challenges in terms of accountability, bias, and transparency. 

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Learning Goals

After completing the certificate, students will be able to:

  1. Create and formulate domain-specific questions and connect them with appropriate data sets;

  2. Acquire, curate, and process data to make them amenable to data analysis;

  3. Demonstrate a basic understanding of data-science techniques including inference, data-based modeling, machine learning, and visualization;

  4. Implement algorithms and data-science techniques in Python;

  5. Apply statistical algorithms to data to extract meaningful information and answer real-world questions;

  6. Demonstrate critical thinking and skepticism in data analyses;

  7. Communicate outcomes effectively using exploratory or explanatory visualizations;

  8. Understand the impact of data science on society, including issues of bias, fairness, and accountability.

Experiential Requirement

The experiential learning component provides students with the opportunity to apply their data-science skills in their concentration, engage in research that uses data science, teach data science as UTAs, or undertake an internship that has a data-science component.

Options for the Experiential Learning Component

  • Internship or summer research: Undertake a summer research experience or an internship that includes a data science component.
  • Independent study course: 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).​
  • Teaching experience: Engage in a reflective teaching experience in a course that has a significant data science component. Options are
    • Serve as an undergraduate teaching assistant or as a UTRA recipient to help create new content, introduce new data sets into a course, or add online modules that connects to additional domain disciplines;
    • Serve as an undergraduate teaching assistant during a semester and use DATA 1150 to satisfy the reflective aspect of the experiential learning component (in this case, DATA 1150 cannot be used as an elective).


  • Students are responsible for identifying an appropriate experiential learning  component.
  • 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 10-15 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.


Students may only declare a certificate once they have an approved concentration on file.

Students must have completed or be enrolled in two courses toward the certificate by the time the certificate is declared.

Students may declare a certificate no earlier than the beginning of the fifth semester, and no later than the last day of classes of the antepenultimate (typically the sixth) semester.

Students must submit a proposal for their experiential component experience by the end of the sixth semester. 

Student Concentrations

Students in the following concentrations have completed the Data Fluency Certificate:

  • History
  • Biology
  • Neuroscience
  • Cognitive Neuroscience
  • Behavioral Decision Sciences
  • Engineering
  • Biomedical Engineering
  • Social Analysis and Research
  • Public Health
  • Geology
  • Urban Studies
  • Geological Sciences
  • English
  • Economics
  • Business
  • Entrepreneurship
  • Political Science
  • Business, Entrepreneurship, and Organizations
  • Neuroscience
  • Anthropology