To come up with a good research question for the 216 Final Project, try testing your potential questions against the following criteria:

1. Is your research question clear?
   1. Does your research question present the problem with enough context such that the person reading it doesn’t need to go on an internet goose chase to understand it?
2. Is your research question focused?
   1. Is your question direct, and address specific points? Is it appropriate for the scope of the project (i.e., timeline, skills, etc.)? 
   2. Can the question be answered thoroughly through this project?
3. Is your research question concise?
   1. Make sure the wording of your question is to the point – don’t be verbose here, as you’re laying out the groundwork for the rest of your project
4. Is your research question complex?
   1. Similar to above, can it be answered thoroughly within the limits of the paper but also not be too simple (i.e., yes or no, questions that can be answered through a simple linear regression)?
   2. Does your question require a sophisticated analysis that is potentially beyond the scope of this class?
5. Is your research question arguable?
   1. Can you take a stance and make an argument in your answer? Can your question be tested?
6. Is your research question analytical?
   1. Does your research question result in a description of the problem or an analysis of the problem?

Examples of GOOD research questions:

1. How do the percent of COVID deaths vary depending on the race of the person across the counties in the US? How does the percent of COVID deaths differ in Republican (red) and Democrat (blue) counties? How do percent of COVID deaths differ based on race and the political party at the county level? 
   1. You don’t necessarily need to have multiple research questions (this depends on the depth of the analysis needed per question, how the questions are related, etc.), but these questions all fulfill each of the above criteria individually and work well together to meet the scope of the project.
2. Is there a positive relationship between ______ and ______ in a country? Does a higher prevalence of _____ correlate with ________ in a country? Is there a negative relationship between  ________ and _________ in a country?
   1. While this research question has several sub-questions, each question is essentially a ‘yes’ or ‘no’ response. It’s not the strongest question, but if you opt for this type of question, we recommend using multiple methods from the course to answer the questions and compare your results. Alternatively, consider replacing the words “is there a positive relationship” to “what is the relationship and how does it vary,” or something that opens the question up to more than binary responses.
3. What is the relationship between vaccine hesitancy and COVID-19 deaths? What is the prevalence of vaccine hesitancy among the general population? What is the impact of political beliefs on vaccine hesitancy? Do factors such as race have any relation to vaccine hesitancy?
   1. Again, multiple questions that mostly meet the above criteria — the last question could be answered with a ‘yes’ or ‘no,’ but you could easily replace it with “which factors have the strongest relationship with.” All other questions invite deeper analysis and the formulation of an argument.

Due: Saturday, February 8th

General Directions

The purpose of this document is to ensure that your group is choosing a substantial research project topic that is interesting and worthwhile. You will be working on the collaborative final project for a large portion (~14 weeks) of this course, and will use this deliverable to brainstorm project ideas and plan how your team will collaborate. In terms of length, it should be 1-2 pages (not including the appendix) using standard margins (1 in.), font (11-12 pt), and line spacing (1-1.5). You should convert your final document to a pdf and upload it to Gradescope under the assignment “Initial Plan” by the due date. Be sure to include your names and NetIDs in your final document and use the group submission feature on Gradescope to include all of your group members in a single submission.

The Initial Plan is out of 100 points. Meeting basic formatting requirements is worth 40 points and will be graded as follows:

• E (Exemplary, 40pts) – Work that meets all requirements. NetIDs and names of all group members are included in the report.
• S (Satisfactory, 38pts) – Work that meets most of the requirements. NetIDs or names of some group members are missing.
• N (Not yet, 24pts) – Does not meet all requirements. NetIDs or names of all group members are missing.
• U (Unassessable, 8pts) –  Missing at least one section.

Part 1: Brainstorming (40 points)

As you go through your brainstorm, consider reading over this guide to forming your research question. To brainstorm ideas for your research topic, you may use one of two options:

1. Mind map of potential project ideas.
2. Discussion with ChatGPT or LLM of your choice.

For the mind map, you can use an online tool, Google drawing, whiteboard, post-it notes, etc. Just ensure you can put it in your report. To create your mind map, use the following steps:

1. Put a central idea or main concept in the center, such as “data science research project” or something more specific that your group finds interesting.
2. Branch out from the main with ideas that can cover a range from interesting topics to previous project ideas that caught your group’s attention.
3. Branch off of those ideas to add more specific interests or personalized ways you would change a topic or project.
4. Put your mind map (if it’s on something like a physical whiteboard take a picture) as an appendix in this submission.

For the discussion with an LLM, do the following:

1. Tell the LLM you are brainstorming for data science projects, what your group’s interests are that could be potential sources of data, and that you need to find the data yourself.
2. Ask it what ideas it has for your project.
3. Tell it what ideas you liked, didn’t like, why a suggestion isn’t a good one, etc.
4. Do at least 2-3 rounds of steps 2 and 3 with the LLM.
5. Put your chat as an appendix in this submission.

After your brainstorm (regardless of if you used the mind map or an LLM), reflect by answering the following questions:

1. Why did you choose the method you used?
2. What patterns do you see in what you find interesting?
3. What research topics or questions did your group generate from this brainstorming? Which of these ideas can you see your group potentially pursuing?
4. Do you feel like more brainstorming is needed before you find a topic?
5. If you used
   1. The mindmap: Did you find your brainstorming narrowing or diverging as you discuss ideas to write down?
   2. LLM: How satisfied were you with its answers? Why?

Whether you choose to create a mind map or use an LLM, use this exercise to brainstorm project ideas that your group collectively believes are interesting, relevant, and worthwhile to your time in this course.

Grading

• E (Exemplary, 40pts) – Appendix has a mind map that branches out at least two levels from the center OR an LLM conversation. In addition, the report has a reflection that comprehensively answers all 5 questions.
• S (Satisfactory, 38pts) – Appendix has a mind map that branches out at least two levels from the center OR an LLM conversation. In addition, the report has a reflection that mostly answers all 5 questions.
• N (Not yet, 24pts) –  A brainstorm that does not entirely answer 1 or 2 of the questions. Reflection does not entirely answer at least 1 of the questions.
• U (Unassessable, 8pts) – Work that does not entirely answer 3 or more of the questions above for either the brainstorm or the reflection.

Part 2: Collaboration Plan (20 points)

This is a collaborative course project pursued by a team of students who bring different strengths and interests to the table. This reflects the reality that significant real-world projects in data science are almost always pursued by teams. For the collaboration to be successful, it helps to establish some guidelines/group norms that serve as a starting point. Your collaboration plan should address the following:

1. How will you divide responsibilities? Will some students be responsible for certain portions of the project, or will you be more integrated and decide on responsibilities on a weekly basis?
2. About how much time do you expect every group member to spend on the project each week, on average? It is okay if this number is higher toward the last couple of weeks of the semester.
3. When and how will you meet? You should plan to meet at least once per week for at least 30 minutes to check in on one another’s progress, get help, and plan for what comes next. Identify a day of the week, a time, and the place/platform you will use to meet. We strongly recommend having a consistent time and not having ad-hoc times as needed.
4. What platform(s) will you use to communicate between meetings? Will you primarily use email, text, Slack, or other chat apps? If you want a more professional enterprise tool, Duke provides free access to Microsoft Teams.
5. Where will you track who is doing what tasks and when those tasks will be done? This can be as simple as a Google doc with a checklist or as advanced as a Trello board. What is important is there is a clear repository of who is doing what, the status of that thing, and when it should be done.
6. Where will you store data, code, writing, etc., so that all group members have easy access to shared materials?* Duke provides free access to Box and GitLab, which could serve these purposes, but you could also use external services like Google Drive or GitHub. Provide a link to the folder/repository in your proposal to demonstrate that it is created and ready.
7. Is your group willing to publicly share your project, for example, as part of a portfolio of work? If yes, how will you share? How will you articulate authorship and who did what? When will you revisit this near the end of the semester to confirm you all still agree to what you write in this initial plan?

* In addition to a common repository for data, you may find it useful to explore Google colab or DeepNote, which allows you to collaborate on Jupyter Notebooks and execute them in the cloud (like a Google doc for Jupyter notebooks).

Grading

• E (Exemplary, 20pts) – Comprehensive plan that answers all 6 questions and includes a link to their folder/repository.
• S (Satisfactory, 18pts) – Comprehensive plan that mostly answers all 6 questions. The link to their folder/repository could be missing.
• N (Not yet, 12pts) – A plan that does not entirely answer 1 or 2 of the questions above. Link can be missing.
• U (Unassessable, 4pts) – A plan that does not entirely answer 3 or more of the questions above.

Due: Friday, January 24th

In place of a final exam, this course has a collaborative final project where we ask you to bring your data science skills to bear on a research project of your own choosing. It is time to start forming groups (of 4-5 students) for the project. Fill out the group formation quiz on Gradescope no later than Friday, January 24th.

The form should only take a couple of minutes. If you already know who you want to work with, you can indicate that in the form using the group submission feature in Gradescope. In this case, communicate with your group first and have one member fill out the form once with everyone added as group members. If you submit more than once, the active submission is considered valid. It’s also fine if you don’t know who you want to work with, in which case you should fill out the form solo, and we will match you to a group.

If it is helpful to start thinking about possible project ideas, below are some project ideas. You can also brainstorm now using strategies that are outlined in the Initial Plan post (out soon). But it is not required that you have a concrete project idea until the proposal.

Project ideas

Not sure how to get started? Looking for examples of what a data science project might look like? Here are some of the topics that students studied in Spring 2020:

• Comparing Stock Market Losses between SARS and SARS-CoV-2
• Recessions, Depressions, and Depression: Mental Health in Relation to Economic Factors
• Predicting North Carolina Election Outcomes
• Relating Text Analysis of Corporate Reports and Stock Performance
• Modeling Consumer Flight Behavior Based on Economic Indicators
• Predicting COVID-19 Death Tolls from Google Search Trends
• Sentiment Analysis of COVID-19 Tweets
• Economic Status and Drug Overdose in North Carolina
• Analyzing Gender and Tech Careers
• Political Landscape According to Social Media
• Forecasting Market Shocks and Performance using Article Headlines
• Tracking Recidivism in US Prisons
• Understanding AirBnBs impact on Evictions
• Understanding Musical Tastes (Music Recommender System)
• Human Impact on Climate since the Industrial Revolution
• The Troll Toll: An Investigation into Troll Tweets

And here is an archive of summer Data+ projects from the last several years. In Data+, teams of about 4 undergraduate students collaborate over the summer on a data science project. You should be able to see final presentations and/or executive summary slides for most projects; feel free to browse for inspiration.

Example Data Sources

Below, we have some examples of datasets or where you might find data. You should work with data that is interesting to you and should feel free (strongly encouraged even) to look for sources yourself. These are listed just as possibilities and starting places.

• Data.gov has a huge compilation of data sets produced by the US government. The US Census Bureau also publishes datasets from all of its survey work. Similarly, The Supreme Court Database tracks all cases decided by the US Supreme Court, and GovTrack.us provides links to all kinds of information about the US Congress and all votes casted by its members.
• Duke University Library Digital Repository Research Data
• ICPSR – An international consortium of more than 750 academic institutions and research organizations, Inter-university Consortium for Political and Social Research (ICPSR) provides leadership and training in data access, curation, and methods of analysis for the social science research community. ICPSR maintains a data archive of more than 250,000 files of research in the social and behavioral sciences. It hosts 21 specialized collections of data in education, aging, criminal justice, substance abuse, terrorism, and other fields.
• The University of California Irvine maintains a large UCI ML repository of publicly contributed datasets aimed toward machine learning tasks of all types. They range from small simple example datasets to large and complicated datasets from specific scientific domains.
• Kaggle maintains several thousand public datasets of interest in a variety of topics. Kaggle also hosts several prediction challenges; one idea for a machine learning project is to enter one of these competitions as a team.
• The Yelp Dataset is provided by Yelp as a research challenge with lots and lots of data about reviews, businesses, images, and cities – text data, rich json data, etc.

Due: Wednesday, 4/23 – NOTE: there will be no regrade window for this milestone.

Please fill out this form if you would like written feedback on the milestone.

General Directions

The project video presentation is intended to provide a high-level overview of your project to an audience of your peers (that is, individuals who have a reasonable knowledge of data science but are not experts in your particular project topic, so presentations should avoid excessive technical jargon). The presentation should demonstrate your ability to communicate the significance and interpret the findings of your research project. The presentation should stand on its own so that it makes sense to someone who has not read your proposal or prototype. Additionally, since the video is a way to share your project with those outside of the class, make sure to discuss if your group is willing to have the video be public (and what kind unlisted or searcahble) so it can be part of someone’s portfolio of work.

Your group should create a video recording of your presentation in which every group member speaks and in which you use a visual aid such as presentation slides. The easiest way to do this is to simply hold a Zoom call with all members of your project group, share your screen with your presentation slides, and record either locally or to the cloud (see Zoom recording help information). If this is not possible, you can also record portions individually and combine the recordings (though this will require additional editing work). In the end, we will ask for a URL to your complete recording, so you can either provide a share link to a Zoom cloud recording or you can record locally and then upload your recording to Duke Box, Google Drive, Warpwire, or any other cloud platform that we can access such that we can view your recording directly online (we should not need to download to view the recording). Ensure that anyone with the link can view the recording.

In terms of length, the presentation should be between 8 and 12 minutes. You can have as many slides as are necessary, but a typical pace has 1-2 slides per minute, so 8-24 slides total would be reasonable. Your slides should prioritize well-labeled figures or visualizations and use text sparingly to emphasize important points. The text should also be large enough that it is reasonably easy to read. Keep in mind that informative presentations often tell a story to their audience, using data to guide viewers. When you are finished, you will submit a pdf of your slides to Gradescope under the assignment “Project Video Presentation.” Be sure to include your names and NetIDs in your final document and use the group submission feature on Gradescope.  Your first slide should include the URL where we can view the recording of your presentation.

Grading

• E (Exemplary, 10pts) – Video presentation is between 8 and 12 minutes.
• S (Satisfactory, 9 pts) – Video presentation is over 12 minutes.

• N (Not yet, 6pts) – Video presentation does not reach 8 minutes.
• U (Unassessable, 2pts) –  Video presentation is missing or does not demonstrate meaningful effort.

Part 0: Title Slide

The very first slide of your presentation should be a title slide containing at least the below information. It does not need to be in the actual video recording.

• A descriptive title of your project/presentation, not “CS216 Presentation Video”
• Names of all group members
• URL to the video recording of your presentation

Grading

• E (Exemplary, 10pts) – Work that meets all requirements.
• S (Satisfactory, 9pts) – The title is not descriptive but meets all other requirements.
• N (Not yet, 6pts) – Does not meet all requirements. URL for video recording is missing.

Part 1: Introduction and Research Questions

Your presentation should begin by introducing your topic generally and posing your research questions. Provide some explanation of the relevance or motivation of your research questions. These slides should serve to provide context surrounding the questions and potential broader effects of the problem.

Grading

• E (Exemplary, 20pts) – General introduction to topic and clearly defined research questions and their motivations.
• S (Satisfactory, 19pts) – General introduction to topic and clearly defined research questions. Discussion of motivations may be missing.
• N (Not yet, 12pts) – General introduction to topic. Research questions and motivations are not clearly defined.
• U (Unassessable, 4pts) – Introduction and research questions are missing or do not demonstrate meaningful effort.

Part 2: Data Sources

Discuss the data you collected and used 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 (i.e., cleaning).

Grading

• E (Exemplary, 20pts) – Origins of data are properly specified, cited, and include discussion of what information they contain. Any relevant data wrangling, cleaning, or other data preparation is explained.
• S (Satisfactory, 19pts) – Origins of data are properly specified, cited, and include discussion of what information they contain. Any relevant data wrangling, cleaning, or other data preparation may be missing or could be improved.
• N (Not yet, 12pts) – Poorly specified data sources and lack of discussion of preparing the dataset.
• U (Unassessable, 4pts) – Discussion of data sources and data preparation are missing or do not demonstrate meaningful effort.

Part 3: Results

Describe your results. Where possible, provide well-labeled and legible charts/figures in your slides to summarize results instead of verbose text. Interpret the results in the context of your research questions. It may not be possible to describe every individual result from your project in a brief amount of time or present every figure created. Focus on the most important and essential results for addressing your research questions. Please note that a screenshot of your dataset does not count as a table or figure and should not be included in your video presentation.

Unlike your final report, it is not generally possible to describe your methods in sufficient detail in a short presentation so that an informed audience member could reproduce your results. Instead, you should focus on your results and their interpretation, and only discuss methods at a high level such as may be necessary to interpret the results.

Example of Interpreting results

Do not: “When we conducted our hypothesis test, we found that p < 0.05, so our results are significant.”

Do: “Since our p-value is significant, we could determine that generation 1 pokemon have a different popularity than all other pokemon. And since the mean popularity of generation 1 is higher than the mean of all the other pokemon, we can conclude that generation 1 is on average more popular.” [The slide shows the p-value]

Grading

• E (Exemplary, 20pts) – Most important and essential results are thoroughly discussed using labeled tables or figures followed by an interpretation of the results in the context of the research questions. 
• S (Satisfactory, 19pts) – Results are thoroughly discussed using labeled tables or figures followed by an interpretation of the results in the context of the research questions. Maybe missing an important result that should have been included.
• N (Not yet, 12pts) – Results are discussed using tables with missing labels or lacking interpretation in the context of the research questions.
• U (Unassessable, 4pts) – Results are missing or do not demonstrate meaningful effort. 

Part 4: Limitations and Future Work

You should briefly 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. You may want to consider any ethical implications or acknowledge potential biases in the results. 

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 audience member (such as a peer in the class) could improve on and extend your results.

Grading

• E (Exemplary, 20pts) – Comprehensive and explicit discussion of important limitations and caveats to results. Brief discussion of future work and how results could be extended and improved upon.
• S (Satisfactory, 19pts) – Comprehensive and explicit discussion of important limitations and caveats to results. Discussion of future work and how results could be extended and improved upon may lack some specification.
• N (Not yet, 12pts) – Incomplete discussion of important limitations and caveats to results. Discussion of future work and how results could be extended and improved upon may lack some specification.
• U (Unassessable, 4pts) – Limitations and future work are missing or do not demonstrate meaningful effort.

Checklist Before You Submit:

1. Is your video presentation between 8 and 12 minutes in length?
2. Does your first slide satisfy all requirements?
   1. A title of your project/presentation
   2. Names of all group members
   3. URL to the video recording of your presentation
3. Do you have an Introduction and clearly stated Research Question(s)?
   1. Do you feel as if this part meets the requirements of E (Exemplary) or S (Satisfactory)?
4. Have you properly specified/cited one or more specific Data Sources and justified why they are relevant to the research Questions?
   1. Do you feel as if this part meets the requirements of E (Exemplary) or S (Satisfactory)?
5. Have you reported all of your important Results, including an interpretation of them in the context of the research questions?
   1. Do you feel as if this part meets the requirements of E (Exemplary) or S (Satisfactory)?
6. Have you defined clear Limitations to your results and Future Work?
   1. Do you feel as if this part meets the requirements of E (Exemplary) or S (Satisfactory)?