Dear @ids-s1-21/TROUT1 -- 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.
Rather than reproduce the data dictionary, you could only highlight the ones that are interesting and refer the reader to look at the data dictionary for the rest (e.g. "see data dictionary"). Definitions could be weaved into the text when they are first introduced.
Data cleaning clear - nice use of a visualiation to aid understanding. Did you make this visualisation yourself or is it from somewhere (needs a citation if some somewhere else!)
It would be good to see the tables referred to in text: "Table showing correlation between Area and Price over all large regions:"
Graphs that lead nowhere are not really important to report - its only really modelling where you report null results.
You mention "a relatively high adjusted r squared value of 0.575." but don't give enough details about the model to intrepret this fully. Unless you are trying to report a "correlation coefficient" which is also an "r" but is not the same as R^2?
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")
Interpretation (2/3 points)
Addresses the project questions or hypothesis, and is supported by the results.
Nice interpretations of the findings, particularly clear in the conclusions
Sticks to the aims of the project
Due to some unclear information its not always possible to know if the interpretations are fully supported by the results.
Creativity and Critical Thought (2/3 points)
Is the project carefully thought out? Are the limitations carefully considered? Does the project demonstrate originality of thought or approach?
Limitations of the data source outlined and how that influences the interpretations.
However it would have been good to have more detailed critical evaluation of the modelling you did as currently there are a lot of details that are unclear there (e.g. model fit, residuals, ect.)
Concise/use of Language (2/3 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?
Maybe could have had more of an introductory hook to get the reader invested in the project and grab their attention.
"Data & Interpretation" and "Map & Interpretation" repeated information
Language use and style is clear, particularly around describing the data and interpetations.
Dear @ids-s1-21/TROUT1 -- 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 (2/3 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 (2/3 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