Grading & Evaluation¶
This course follows a mastery-based grading approach as summarized below. Students work towards their desired mastery level, and the overall quality of their deliverables determines the final grade.
| Grade | Description |
|---|---|
| A | Complete all required and all challenge exercises with full points on them. |
| A- | Complete an original, novel, and high-quality final course project (See notes below). The overall quality of the project and exercises determines “A,” “A-,” or lower. |
| B+ | Complete all required exercises and at least 6 challenge exercises with full points on them. Complete a high-quality final course project (See notes below). Overall quality of the deliverables determines “B+” or lower. |
| B | Complete all required exercises and at least 4 challenge exercises with full points on them. Overall quality of the deliverables determines “B” or lower |
| B- | Complete all required exercises and 2 challenge exercise with full points on them. Overall quality of the deliverables determines “B-” or lower. |
| Cs & Ds | Not fulfilling the requirements for “B-”; inadequate performance. |
Notes:
- Students aiming to complete a final project must submit a brief proposal and receive approval before the completion of the 3rd Module of the course (i.e., halfway through the course).
- Projects are student-driven and a baseline of high-quality work is expected (for “B+” mastery). Students seeking to demonstrate a high level of mastery (“A/A-”) must complete a high-quality project that incorporates original and novel contributions. Merely pursuing a final project does not guarantee “A/A-/B+” grade. Students are expected to work iteratively (i.e., in an agile manner and not in a once-and-done fashion) and proactively seek feedback on interim drafts.
- There is no one-size-fits-all template or rigid structure for student projects. Students must consider the course project as a personalized learning pathway. Some typical types of projects are programmatic data analysis (using Python or R or similar programming languages), predictive modeling (supervised or unsupervised machine learning), developing a detailed business case and plan for deploying an emerging technology in a real-world firm or industry context, and examining policy and regulatory issues related to an emerging technology. For projects that involve data analysis, students should not expect the instructor to provide readily accessible data; both publicly available data (e.g., census bureau, CDC, Pew research, or other professional organizations) and proprietary data from workplaces are acceptable.