The Certificate in Data Fluency provides a curricular structure to undergraduates in concentrations other than applied mathematics, computational biology, computer science, math, and statistics who wish to gain fluency and facility with the tools of data analysis and its conceptual framework. The driving intellectual question is how we can infer meaning from data whilst avoiding false predictions. 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 an undergraduate teaching assistant, or undertake an internship that has a substantive data-science component.
As with all undergraduate certificates, students must be enrolled in or have completed at least two courses toward the certificate at the time they declare in ASK, which must be 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, in order to facilitate planning for the 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 (including joint concentrations in these areas)
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 | |
| 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 | ||
| Statistical Inference I | ||
| Statistical Analysis of Biological Data | ||
| Methods in Informatics and Data Science for Health | ||
| Survey of Biomedical Informatics | ||
| Statistical Methods | ||
| Computational Methods for Mind, Brain and Behavior | ||
| Probability for Computing and Data Analysis | ||
| Deep Learning | ||
| Data Science | ||
| Data Science Fellows | ||
| Introduction to Econometrics | ||
| Big Data | ||
| Introductory Statistics for Education Research and Policy Analysis | ||
| Introduction to Environmental GIS | ||
| Introduction to Geographic Information Systems for Environmental Applications | ||
| Global Environmental Remote Sensing | ||
| Probability | ||
| Essentials of Data Analysis | ||
| Principles of Biostatistics and 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. 1 | ||
| Total Credits | 4-5 | |
| 1 | Students must submit a proposal for their practicum project by the end of the sixth semester. |