Course Projects

Students are required to form teams of 2 people to work on a course project.

Timelines

  • Form a team of two to work on the project.

  • Find a dataset of interest to you.

  • Turn in a brief one-page description by the end of week 3 of Sept. 8th. (points: 3/30)

  • Submit a mid-term report (2 - 4 pages, no more than 4 please) by the end of week 12 of Nov. 10th. (7/30)

  • Present your work to your peers week 15 and 16. (10/30)

  • Submit a final report (4 - 8 pages, no more than 8 please) to xji4@tulane.edu via email by December 14.

  • Submit code to your own private GitHub repository on the course GitHub organization by December 14. (Report + Code, 10/30)

  • (Optional, 5 bonus points towards total grade for each individual in team) Make a GitHub page for your project and demo in final presentation.

Project ideas/Dataset resources

Amazon data http://jmcauley.ucsd.edu/data/amazon/, https://nijianmo.github.io/amazon/index.html, https://cseweb.ucsd.edu/~jmcauley/datasets.html

Netflix challenge https://www.kaggle.com/datasets/netflix-inc/netflix-prize-data

Kaggle https://www.kaggle.com/

Sports/eSports prediction

Reproduce findings of a paper in your field (could be extremely hard).

Google “data science projects” to get more ideas

Brief Description components

  • Introduce the dataset (data type, origin, etc). Explain why you choose the dataset. List some questions you want to explore with the dataset.

Mid-term report components

  • Include the brief description with modifications if needed

  • Give an abstract on your plan

    • What analyses you want to perform for answering your questions

  • Current progress and future plan

Final report components

  • Introduce the dataset. Explain why you choose it. Explain what questions you want to ask and explore using the dataset.

  • Analysis. Explain the statistical methods that you use for analyzing the dataset. Explain what you have done to generate the results (make your analysis reproducible).

  • Results. Illustrate your results. Use figures and tables to imiprove readability.

  • Discussions. This is the place to put in almost whatever you want to share. Some difficulties you met in the analysis, what you learned from the analysis, some future directions.