Your project will be assessed using the following standards, as defined by the course syllabus and the data science workflow:
- Identify & Acquire
Requirements for these standards will be assessed using the scale below:
| Score | Expectations |
|---|---|
| 0 | Incomplete |
| 1 | Does not meet expectations |
| 2 | Meets expectations, good job! |
| 3 | Exceeds expectations, you wonderful creature, you! |
Acceptable performance for this standard is based on how well you've applied specific learning goals within your deliverable. To review the full list of data science standards, see the course syllabus.
While your total score may serve as a helpful gauge of whether you've met project goals, specific standards scores are more important since they can show you where to focus your efforts in the future!
Meets Expectations: Did you: Demonstrate comprehension of project objectives? Articulate the goals and criteria for success? Create guiding questions to identify data and potential methods of analysis? Application of these learning goals will be assessed using the requirements below:
Mark boxes with an 'X'
| Requirements | Incomplete (0) | Does Not Meet Expectations (1) | Meets Expectations (2) | Exceeds Expectations (3) |
|---|---|---|---|---|
| Well-articulated problem statement with "specific aim" and hypothesis, based on your lightning talk | ||||
| An outline of any potential methods and models | ||||
| Detailed explanation of extant data available | ||||
| Describe any outstanding questions, assumptions, risks, caveats | ||||
| Define your goals and criteria, explain what success looks like | ||||
| Demonstrate domain knowledge, including features or benchmarks from similar projects |
Notes:
Based on all requirements, you can earn a maximum of 18 points on this project.
Student Check-in:
| HIGHLIGHTS | GROWTH OPPORTUNITIES | DEVELOPMENT PLAN |
|---|---|---|