Evaluating your project prior to submission#
What is this document for?#
As you work on finishing your project, it is important that you reflect on what work your have left to do and how you might make your project even better than it is currently. You can use this document, along with the feedback your receive from your peers in class previously, to improve your project before your submit the final version.
Because Jupyter notebooks allow for a synergy between and interwoven written narrative alongside executable code and data visualizations, there are a variety of criteria that can be used for evaluation. The following categories are include some of the things we will be looking for when we asses the final version of your project, you should look carefully are your project draft and make sure you’ve addressed all of these points. Also, as a reminder, you can see the grading rubric and expectations for the project here.
Criteria for evaluation#
For each of these criterion, review your project and, if it seems valuable, write yourself a set of notes that you think could be used to improve your project. Then, use these notes to improve your project.
Does the notebook run?#
The submitted notebook (along with auxiliary materials) should run top to bottom without errors. In your Jupyter Notebook, go to “Kernel” and select “Restart & Run All”. Make sure you do this before you turn in your final submission as well. Also, make sure you submit the fully executed version of the notebook.
If there is a reason why it can’t be run, you should include a note at the beginning of the notebook explaining why so that you instructor understands this limitation (e.g., “The dataset is too large to share via D2L, but can be downloaded from [this link]” or “The data is only available locally on my personal computer”).
Clear research question#
Is the research question clearly stated at the beginning? Is it easy to find?
Clear methodology#
You should unpack/explain your steps along the way. Your audience are your fellow CMSE 801 students and your instructors, meaning that any tool and/or package must be thoroughly explained, both in terms of what it does and what it produces.
Informative and well-designed plots#
Use the data visualization content from class to evaluate the effectiveness of the plots you’ve included and presented.
Compact code#
There shouldn’t be any large chunks of repeated code or copy-pasted code. If the same procedure is used multiple times, think about how it could be turned into a function or accomplished using a loop.
Informative and unique variable names, code comments to help guide the reader#
The variables used in the code should indicate their purpose and make it easy for another reader to understand a bit about what your code is doing (i.e., you shouldn’t see a lot of “a”, “x”, or otherwise non-descriptive variables). Also, watch out for repeated variable names as this can make the code hard to follow and lead to confusing bugs. Comments in your code can also help someone understand what your code is doing.
Reflecting on your self-evaluation#
After your think through or take some notes on these criteria, you should work to make changes that improve your project.