Brown University
Source: https://bulletin.brown.edu/the-college/undergraduatecertificates/dtfl/ Parent: https://bulletin.brown.edu/the-college/undergraduatecertificates/
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 certificateand 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 | |
| ANTH 1201 | Introduction to Geographic Information Systems and Spatial Analysis | |
| APMA 1650 | Introduction to Probability and Statistics with Calculus | |
| BIOL 0495 | Statistical Analysis of Biological Data | |
| BIOL 1555 | Methods in Informatics and Data Science for Health | |
| BIOL 1565 | Survey of Health Informatics | |
| CPSY 0900 | Statistical Methods | |
| CPSY 1291 | Computational Methods for Mind, Brain and Behavior | |
| CPSY 1580C | Visualizing Information | |
| CSCI 1411 | Foundations of AI | |
| CSCI 1420 | Machine Learning | |
| CSCI 1450 | Advanced Introduction to Probability for Computing and Data Science | |
| CSCI 1470 | Deep Learning | |
| CSCI 1951A | Data Science | |
| DATA 1030 | Hands-on Data Science | |
| DATA 1150 | Data Science Fellows 1 | |
| DATA 1500 | Data Visualization & Narrative | |
| ECON 1620 | Introduction to Econometrics | |
| ECON 1660 | Big Data | |
| EDUC 1230 | Applied Statistics for Ed Research and Policy Analysis | |
| ENVS 1105 | Introduction to Environmental GIS | |
| EEPS 1320 | Introduction to Geographic Information Systems for Environmental Applications | |
| EEPS 1330 | Global Environmental Remote Sensing | |
| EEPS 1340 | Machine Learning for the Earth and Environment | |
| HIST 1825J | History of Artificial Intelligence | |
| MATH 1210 | Probability | |
| MUSC 1210 | Seminar in Electronic Music: Real-Time Systems | |
| PHP 1501 | Essentials of Data Analysis | |
| PHP 1510 | Principles of Biostatistics and Data Analysis | |
| PHP 1560 | Using R for Data Analysis | |
| SOC 1020 | Methods of Social Research | |
| SOC 1100 | Introductory Statistics for Social Research | |
| SOC 1340 | 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.