The Certificate in Data Fluency provides a formal pathway for undergraduates in concentrations other than applied mathematics, computational biology, computer science, math, and statistics (see details below) who wish to gain fluency and facility with the tools of data science. The driving intellectual question motivating certificate students is how we can infer meaning from data whilst avoiding false predictions. The required experiential learning component provides you with the opportunity to apply your data-science skills in applied settings, engage in research that uses data science, teach data science as an undergraduate teaching assistant, or undertake an internship that has a substantive data-science component.
As with all undergraduate certificates, the certificate has the following requirements:
- Students may not earn more than one certificate and may only have one declared concentration.
- Students must be enrolled in or have completed at least two courses toward the certificate at the time they declare in ASK.
- No more than one course may count toward your concentration and the certificate.
- Students may declare in ASK no earlier than the beginning of the fifth semester and must declare no later than the last day of classes of the antepenultimate (typically the sixth) semester, in order to facilitate planning for the capstone or other experiential learning opportunity.
- Students must submit a proposal for their experiential learning opportunity by the end of the sixth semester.
Excluded Concentrations: Applied Mathematics, Computational Biology, Computer Science, Mathematics, and Statistics. This includes joint concentrations in these areas; for example, Applied Mathematics-Economics is also excluded. According to the certificate guidelines, a student’s concentration and certificate cannot have substantial overlap.
For more information on the Certificate in Data Fluency, please visit the Data Science Institute website.
Certificate Requirements
Certificate Requirements
| Core Courses: | ||
| DATA 0080 | Data, Ethics and Society | 1 |
| CSCI 0111 | Computing Foundations: Data | 1 |
| or CSCI 0150 | Introduction to Object-Oriented Programming and Computer Science | |
| or CSCI 0170 | Computer Science: An Integrated Introduction | |
| or CSCI 0190 | Accelerated Introduction to Computer Science | |
| or CPSY 0950 | Introduction to programming | |
| DATA 0200 | Data Science Fluency | 1 |
| Elective Course: Select one follow-up Applied Math, Biostatistics, Computer Science or domain-specific course with a significant data component from the following list (or another course with approval from the certificate advisor): | 1 | |
| Introduction to Geographic Information Systems and Spatial Analysis | ||
| Introduction to Probability and Statistics with Calculus | ||
| Statistical Analysis of Biological Data | ||
| Methods in Informatics and Data Science for Health | ||
| Survey of Health Informatics | ||
| Statistical Methods | ||
| Computational Methods for Mind, Brain and Behavior | ||
| Visualizing Information | ||
| Foundations of AI | ||
| Machine Learning | ||
| Advanced Introduction to Probability for Computing and Data Science | ||
| Deep Learning | ||
| Data Science | ||
| Hands-on Data Science | ||
| Data Science Fellows 1 | ||
| Data Visualization & Narrative | ||
| Introduction to Econometrics | ||
| Big Data | ||
| Applied Statistics for Ed Research and Policy Analysis | ||
| Introduction to Environmental GIS | ||
| Introduction to Geographic Information Systems for Environmental Applications | ||
| Global Environmental Remote Sensing | ||
| Machine Learning for the Earth and Environment | ||
| History of Artificial Intelligence | ||
| Probability | ||
| Seminar in Electronic Music: Real-Time Systems | ||
| Essentials of Data Analysis | ||
| Principles of Biostatistics and Data Analysis | ||
| Using R for Data Analysis | ||
| Methods of Social Research | ||
| Introductory Statistics for Social Research | ||
| Principles and Methods of Geographic Information Systems | ||
| Capstone: | 0-1 | |
The required 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. The capstone may be completed for credit via an independent study course or not for credit. 2 | ||
Options for fulfilling this requirement include: | ||
1. Participate in a Brown University credit experience (i.e. independent study). | ||
2. Participate in a non-credit experience: summer Internship; TA for data-related course; work with a local organization on a data-related project. A 10-12 page reflective paper is required for this option. | ||
3. Be a Data Science Fellow. 1 | ||
| Total Credits | 4-5 | |
- 1
Students may complete DATA 1150 and the concurrent Data Science Fellows project to fulfill both the elective and experiential components of the certificate.
- 2
Students must submit a proposal for their experiential component by the end of the sixth semester.