Certificate in Data Fluency

Description

April 7, 2020 Zoom info session on this certificate: this video covers some of the basic requirements of the certificate, which are also described below. 

The massive increase of data in society, commerce, and science has fundamentally transformed 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, have created possibilities for data analysis on a scale previously unimagined. Many disciplines now require an understanding of how data are collected, stored, analyzed, and visualized. At the same time, the societal impact of decisions made based on data poses many challenges in terms of accountability, bias, and transparency.

The undergraduate Certificate in Data Fluency is for undergraduate students who wish to gain fluency and facility with the tools of data analysis and its conceptual framework. 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. Because of the significant overlap with concentrations in the following disciplines, the certificate is not allowed for students concentrating in Applied Mathematics, Computational Biology, Computer Science, Mathematics, or Statistics.

Courses Required

  • 3 required core courses: DATA 0080, CSCI 0111, DATA 0200
  • 1 elective course
  • 1 experiential learning component

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 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.

For more information, see Brown's Certificate Guidelines

Qeustions? Please contact [email protected].