Due: Wednesday 4/20, Late due Friday 4/22 (no late penalty and no need for homework slip days)
If 85% of the class fills out course evaluations, the report’s late due date will become Saturday 4/23. Note this is a HIGHER number than for the presentation. Go to the course evaluations page to find out how to fill them out.
The final report is intended to provide a comprehensive account of your collaborative course project in data science. The report should demonstrate your ability to apply the data science skills you have learned to a real-world project in a holistic way from posing research questions and gathering data to analysis, visualization, interpretation, and communication. The report should stand on its own so that it makes sense to someone who has not read your proposal or prototype.
The report should contain at least the parts defined below. In terms of length, it should be 5-7 pages using standard margins (1 in.), font (11-12 pt), and line spacing (1-1.5). A typical submission is around 3-4 pages of text and 5-7 pages overall with tables and figures. It is important to stay within the page limit, practicing being succinct is an important skill. You should convert your written report to a pdf and upload it to Gradescope under the assignment “Project Final Report” by the due date. Be sure to include your names and NetIds in your final document and use the group submission feature on Gradescope. You do not need to upload your accompanying data, code, or other supplemental resources demonstrating your work to Gradescope; instead, your report should contain instructions on how to access these resources (see part 3 below for more details).
In general, your approach to this report should be to write as if you had “planned this as your project all along.” A report is not a chronological story of your project. It is a summary of what you did where the “story” serves the reader’s comprehension.
Part 1: Introduction and Research Questions
Your final report should begin by reintroducing your topic and restating your research question(s) as in your proposal. As before, your research question(s) should be (1) substantial, (2) feasible, and (3) relevant. In contrast to the prior reports, the final report does not need to explicitly justify that the research questions are substantial and feasible in the text; your results should demonstrate both of these points. Therefore, you can remove that text to save space.
You should still explicitly justify how your research questions are relevant. In other words, be sure to explain the motivation of your research questions.
You can start with the text from your prototype, but you should update your introduction and research questions to reflect changes in or refinements of the project vision. Your introduction should be sufficient to provide context for the rest of your report.
Part 2: Data Sources
Discuss the data you have collected and are using to answer your research questions. Be specific: name the datasets you are using, the information they contain, and where they were collected from / how they were prepared. You can begin with the text from your prototype but be sure to update it to fit the vision for your final project.
Part 3: Results and Methods
This is likely to be the longest section of your paper at multiple pages. The results and methods section of your report should explain your detailed results and the methods used to obtain them. Where possible, results should be summarized using clearly labeled tables or figures and supplemented with written explanations of the significance of the results with respect to the research questions outlined previously.
Your description of your methods should be specific. For example, if you scraped multiple web databases, merged them, and created a visualization, then you should explain how each step was conducted in enough detail that an informed reader could reasonably be expected to reproduce your results with time and effort. Just saying “we cleaned the data and dealt with missing values” or “we built a predictive model” is not sufficient detail, for example.
Your report should also contain instructions on how to access your full implementation (that is, your code, data, and any other supplemental resources like additional charts or tables). The simplest way to do so is to include a link to the box folder, GitLab repo (if you use GitHub wish to keep the repo private add Prof. Stephens-Martinez (username: ksteph) and your mentor to the repo), or whatever other platforms your group is using to house your data and code.
Part 4: Limitations and Future Work
In this part, you should discuss any important limitations or caveats to your results with respect to answering your research questions. For example, if you don’t have as much data as you would like or are unable to fairly evaluate the performance of a predictive model, explain and contextualize those limitations.
Finally, provide a brief discussion of future work. This could explain how future research might address the limitations you outline, or it could pose additional follow-up research questions based on your results so far. In short, explain how an informed reader (such as a peer in the class) could improve on and extend your results.
Part 5: Conclusion
Provide a brief (one or two paragraphs) summary of your results. This summary of results should address your research questions. For example, if one of your research questions was “Did COVID-19 result in bankruptcy in North Carolina during 2020?” then a possible (and purely hypothetical) summary of results might be “We aggregate the public records disclosures of small businesses in North Carolina from January 2019 to December 2020 and find substantial evidence that COVID-19 did result in a moderate increase in bankruptcy during 2020. This increase is not geographically uniform and is concentrated during summer and fall 2020. We also examined the impact of federal stimulus but cannot provide an evaluation of its impact from the available data.”
(Optional) Part 6: Appendix of additional figures and tables
If you are struggling to keep your report within the 5-7 page limit, you may move some of your figures and tables to an optional appendix that will not count against your page limit. However, your report should stand on its own without the appendix. The appendix is for adding more nuance to your results, not to give you more space to talk about your results. Succinctness is an important skill to practice when doing data science.
Final reports will be evaluated on the following criterion-based rubric. Reports satisfying all criteria will receive full credit.
- Submits a relevant document satisfying general requirements – If you submit a report that is over the page limit (not counting the appendix), you will lose points.
- Includes a brief introduction to the topic of interest
- Poses one or more concrete research questions
- Provides a reasonable justification that research questions are relevant
- Includes a discussion of concrete/specific data sources
- Provides results in the form of analysis, tables, visualization, etc.
- Final tables and visualizations are properly labeled and legible
- Results provide reasonable answers to research questions and interpretation is provided in the text. Some results may be negative or incomplete (with discussion) but should provide some concrete evidence toward answers to research questions.
- Results and methods demonstrate substantial effort and progress over the course of the project
- Methods used to obtain results are described in sufficient detail to understand and interpret results
- Methods used are generally appropriate and do not contain significant methodological errors
- Provides a link/reference to additional materials (e.g., code and data stored in Box or GitLab)
- Provides a reasonable discussion of any limitations to the results
- Provides a reasonable discussion of future work and how the results could be extended
- Provides a conclusion
- Final writeup is edited and polished. Can have one or two typos or grammatical errors, but the document is sufficiently edited as to not distract or confuse the reader.