The team project consists of two modules. Each module requires participants to apply the skills they have learned to date, and explore a dataset of their choosing. The first part of the team project involves creating a simple program with a database in order to analyze a dataset from an open source, such as Kaggle. In the second part of the team project, teams will come together again and apply the skills developed in each of the data science or machine learning foundations certificate streams. Teams will either create a data visualization or a machine learning model.
Participants will work in assigned teams of 4-5.
By the end of Team Project Module 1, participants will be able to:
- Resolve merge conflicts
- Describe common problems or challenges a team encounters when working collaboratively using Git and GitHub
- Create a program to analyze a dataset with contributions from multiple team members
By the end of Team Project Module 2, participants will be able to:
- Create a data visualization as a team
- Create a machine learning model as a team
Questions can be submitted to the #cohort-3-help channel on Slack
-
Technical Facilitator:
- Kamilah Ebrahim(she/her) kamilah.ebrahim@mail.utoronto.ca
-
Learning Support Staff:
- Farzaneh Hashemi (she/her ) fhashemi.ma@gmail.com
- Tong Su (she/her) tong.su@mail.utoronto.ca
Each Team Project module will include two live learning sessions and one case study presentation. During live learning sessions, facilitators will introduce the project, walk through relevant examples, and introduce various team skills that support project success. The remaining time will be used for teams to assemble and work on their projects, as well as get help from the facilitator or the learning support to troubleshoot any issues a team may be encountering.
Work periods will also be used as opportunities for teams to collaborate and work together, while accessing learning support.
| Day 1 | Day 2 | Day 3 | Day 4 | Day 5 |
|---|---|---|---|---|
| Live Learning Session | Live Learning Session | Case Study | Work Period | Work Period |
- Participants are expected to attend live learning sessions and the case study as part of the learning experience. Participants are encouraged to use the scheduled work period time to complete their projects.
- Participants are encouraged to ask questions and collaborate with others to enhance learning.
- Participants must have a computer and an internet connection to participate in online activities.
- Participants must not use generative AI such as ChatGPT to generate code to complete assignments. It should be used as a supportive tool to seek out answers to questions you may have.
- We expect participants to have completed the onboarding repo.
- We encourage participants to default to having their camera on at all times, and turning the camera off only as needed. This will greatly enhance the learning experience for all participants and provides real-time feedback for the instructional team.
|-- data
|---- processed
|---- raw
|---- sql
|-- reports
|-- src
|-- README.md
|-- .gitignore|-- data
|---- processed
|---- raw
|---- sql
|-- experiments
|-- models
|-- reports
|-- src
|-- README.md
|-- .gitignore- Data: Contains the raw, processed and final data. For any data living in a database, make sure to export the tables out into the
sqlfolder, so it can be used by anyone else. - Experiments: A folder for experiments
- Models: A folder containing trained models or model predictions
- Reports: Generated HTML, PDF etc. of your report
- src: Project source code
- README: This file!
- .gitignore: Files to exclude from this folder, specified by the Technical Facilitator