Monthly Archives: August 2024

Project: Initial Plan

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.

Project: Group Formation

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.