Project Team
- Irene Vrbik, Assistant Professor of Teaching, Department of Computer Science, Mathematics, Physics and Statistics, Irving K. Barber Faculty of Science (Lead Applicant)
- Jeff Andrews, Principal Co-director Master of Data Science Program and Associate Professor, Department of Computer Science, Mathematics, Physics and Statistics, Irving K. Barber Faculty of Science
- Patricia Lasserre, Associate Professor Department of Computer Science, Mathematics, Physics and Statistics, Irving K. Barber Faculty of Science
- Bowen Hui, Associate Professor of Teaching and Associate Head Undergraduate, Computer Science, Department of Computer Science, Mathematics, Physics and Statistics, Irving K. Barber Faculty of Science
- Firas Moosvi, Lecturer, Department of Computer Science, Mathematics, Physics and Statistics, Irving K. Barber Faculty of Science
- Lengyi Han, Assistant Professor of Teaching, Department of Computer Science, Mathematics, Physics and Statistics, Irving K. Barber Faculty of Science
- Ramon Lawrence, Professor, Department of Computer Science, Mathematics, Physics and Statistics, Irving K. Barber Faculty of Science
Themes
- Equity, Diversity, and Inclusion
- Professional Skills and Competencies
- Program Development and Transformation
- Teaching Resources
Year
2022
About the Project
In a commitment to training at the forefront of data science, the main goal of this project is to strengthen the current undergraduate and masters program by restructuring its design in terms of learning outcomes. While academic staff will undoubtedly be involved in this process, a thorough literature review, survey of similar degrees, consultation with past and present students, and input from industry will play a crucial role in formulating these outcomes.
Unlike seasoned academic subjects, curriculum standards in this emerging discipline are far from well-established. As we re-examine how to provide the best data science education for our students, targeted effort will be made in incorporating evidence-informed pedagogies into the classroom and creating learning outcomes at the topic, course and program level of abstraction. A curriculum map will be used to align program-level outcomes to specific courses and learning activities/assessments. Any gaps or redundancies identified through this infrastructure will help inform the creation of new courses (e.g. DATA 100, DAT 200), encourage pedagogical innovations (including creation of a comprehensive open problem bank), and evoke change towards a more equitable, diverse, and inclusive environment. By making this organizing structure available online, students can discover different pathways through the program that could have significant impacts on course and career planning. Through the maintenance of this infrastructure at both the undergraduate and master’s level, we hope to foster student and faculty engagement and cultivate continual improvement to reflect the fast-changing field.
Awarded in the 2022 Program Development and Redesign Stream
Additional Information