Dear @ids-s1-21/The_Code_Ninjas -- Below is the feedback for the write-up (executive summary) for the assignment. Please review carefully, and contact the course organisers if you have any questions.
Assessment Rubric
Clarity of Aim (3/3 points)
The work’s aim is stated in a simple manner, questions are concrete and testable.
Methodology Description (1/3 points)
Accurately describes the data and use of data science techniques to meet the aim(s) of the project.
- Would have been good to see the plots referred to in the text - or at least reference where to find them (e.g. "see slide 5"). This is particualtly an issue in this summary because the project generally seems to rely on visualisations that require searching for and looking at the code in some instances to fully understand. Although the description of techniques is limited, the interpretation is good (see below)
Interpretation (2/3 points)
Addresses the project questions or hypothesis, and is supported by the results.
- A good focus on not only describing findings but putting them into context and explaining them with the use of "expert knowledge" (knowledge of the subject area)
- Interpretations could have been better supported with metrics, descriptive statistics ect.
Creativity and Critical Thought (3/3 points)
Is the project carefully thought out? Are the limitations carefully considered? Does the project demonstrate originality of thought or approach?
- The project is creative and well thought through; although alike to the interpretation limitations culd be supported by metrics (but I've already docked a point for that)
Concise/use of Language (3/3 points)
Is it concise but detailed enough (e.g. avoids repetition and wordiness)? Is it understandable for people not familiar with the data? Does it clearly communicate thoughts and concepts whilst utilizing an appropriate language and style?
- Nice introduction concisely reviewing the background needed to understand the work and making the reader interested in the project
- Some specific info about the data could have just been refering the reader to look at the data dictionary (e.g. the number of observations - "see data dictionary")
- Some reordering early on could help with clarity (e.g. paragraph 4 feels like it would work better after the first one to explain why we are looking at the "Big-Mac Index").
- Parts of the data are defined and explained well throughout
- Good style!
Other
Scores
|
Earned |
Available |
| Clarity of Aim |
3 |
3 |
| Methodology Description |
1 |
3 |
| Interpretation |
2 |
3 |
| Creativity and Critical Thought |
3 |
3 |
| Concise/use of Language |
3 |
3 |
| Other |
0 |
-15 |
| Total |
12 |
15 |
Dear @ids-s1-21/The_Code_Ninjas -- Below is the feedback for the write-up (executive summary) for the assignment. Please review carefully, and contact the course organisers if you have any questions.
Assessment Rubric
Clarity of Aim (3/3 points)
The work’s aim is stated in a simple manner, questions are concrete and testable.
Methodology Description (1/3 points)
Accurately describes the data and use of data science techniques to meet the aim(s) of the project.
Interpretation (2/3 points)
Addresses the project questions or hypothesis, and is supported by the results.
Creativity and Critical Thought (3/3 points)
Is the project carefully thought out? Are the limitations carefully considered? Does the project demonstrate originality of thought or approach?
Concise/use of Language (3/3 points)
Is it concise but detailed enough (e.g. avoids repetition and wordiness)? Is it understandable for people not familiar with the data? Does it clearly communicate thoughts and concepts whilst utilizing an appropriate language and style?
Other
Scores