Extra credit
Extra credit submissions must be submitted no later than Monday, December 9th at 11:59pm ET.
Getting started
- Go to the info4940-fa24 organization on GitHub. Click on the repo with the prefix ec. It contains the starter documents you need to complete the assignment
- Clone the repo and start a new project in RStudio.
Tidy Tuesday
Tidy Tuesday is a weekly data project to promote wrangling and visualization skills. It is hosted by the Data Science Learning Community which aims to “create a supportive and responsive online space for learners” to improve their programming and data analysis skills.
Every week they post a raw dataset on GitHub and ask people to explore the data. The ultimate goal is to apply R skills, get feedback, explore other’s work, and connect with the greater #RStats community. Contributors frequently publish their work on social media under the #TidyTuesday hashtag. Datasets are posted on Mondays.
You are expected to solve a predictive problem using a Tidy Tuesday dataset published during 2024. Your submission should include information on all aspects of the ML workflow, including exploratory analysis, data preprocessing, model selection, and evaluation. You should also include a brief written description of your findings and the rationale behind your choices.
Submission
Once you are finished with the assignment, you will upload you final PDF document to Gradescope. You may only submit one extra credit assignment for the semester. Once it has been evaluated, you may not submit another attempt.
To submit your assignment:
- Go to http://www.gradescope.com and click Log in in the top right corner.
- Click School Credentials \(\rightarrow\) Cornell University NetID and log in using your NetID credentials.
- Click on your INFO 4940 course.
- Click on the assignment, and you’ll be prompted to submit it.
- Mark every page to be associated with exercise #1. There will be only one exercise listed.
Grading
Students can earn up to a maximum of 1 percentage point towards their final grade. Evaluations are based on the nebulous Difficulty + Execution scoring system.
| Component | Points |
|---|---|
| Difficulty | 5 |
| Execution | 5 |
The more challenging the work, the more points you will earn. Likewise, the higher-quality the execution of the ML workflow, the more points you will earn. Partial credit may be awarded.