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 a certificate in ASK only once an approved concentration is on file, 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, Computational Neuroscience, Computer Science, Mathematics, Statistics, and Social Analysis and Research. 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: 1 | 1 | |
| 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). Students should be aware of potential prerequisites for these courses, which can be found at Courses@Brown. | ||
| Introduction to Geographic Information Systems and Spatial Analysis | ||
| Introduction to Probability and Statistics with Calculus | ||
| Statistical Analysis of Biological Data | ||
| Computational Methods for Studying Demographic History with Molecular Data | ||
| Survey of Health Informatics | ||
| Methods in Informatics and Data Science for Health | ||
| Artificial Intelligence in Health Care | ||
| Statistical Methods | ||
| Computational Methods for Mind, Brain and Behavior | ||
| Visualizing Information | ||
| Deep Learning in Brains, Minds and Machines | ||
| Foundations of AI and Machine Learning | ||
| Database Management Systems | ||
| Sociotechnical Approaches to AI and HCI | ||
| Machine Learning | ||
| Deep Learning | ||
| Fairness in Automated Decision Making | ||
| Data Science | ||
| Hands-on Data Science | ||
| Data Engineering | ||
| Data Science Fellows 2 | ||
| Data Visualization & Narrative | ||
| Introduction to Econometrics | ||
| Mathematical Econometrics I | ||
| Applied Statistics for Ed Research and Policy Analysis | ||
| Introduction to Geographic Information Systems for Environmental Applications | ||
| Global Environmental Remote Sensing | ||
| Machine Learning for the Earth and Environment | ||
| Tackling Climate Change with Machine Learning | ||
| Probability | ||
| Seminar in Electronic Music: Real-Time Systems | ||
| Methods of Social Research | ||
| Introductory Statistics for Social Research | ||
| Principles and Methods of Geographic Information Systems | ||
| Essentials of Data Analysis | ||
| Principles of Biostatistics and Data Analysis | ||
| Using R for Data Analysis | ||
| Experiential Learning Component: 3 | 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. More information can be found at https://dsi.brown.edu/academics/certificate-data-fluency/experiential-learning-component-elc. | ||
Options for fulfilling the requirement include: | ||
1. Participate in a Brown University credit experience (such as an independent study or the Data Science Fellows course). | ||
2. Participate in a non-credit experience (such as a data-related internship, TA-ing for a certificate course, working with the CEDEC on a data-related project). A reflective paper is required for a non-credit option. | ||
| Total Credits | 4-5 | |
- 1
It is best practice to take the elective with or after the other core courses so that students can integrate their data science skills into the advanced elective.
- 2
Students may complete DATA 1150 and the concurrent Data Science Fellows project to fulfill both the elective and experiential components of the certificate. Students interested in DATA 1150 should pay close attention to the prerequisites and the application deadline for the course.
- 3
Students must submit a proposal for their experiential component by the end of the sixth semester.