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.

• First Team Project Description
• Second Team Project Description

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:

  • Phil Van-Lane(he/him) [email protected]

• Learning Support Staff:

  • Taneea Agrawaal (she/her) [email protected]
  • Farzaneh Hashemi (she/her ) [email protected]
  • Tong Su (she/her) [email protected]

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.

• 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 sql folder, 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

After meeting with the team members, we decided to study a subject related to immigration and cost of rent. Other team member worked on getting a very detailed housing and rent data set. For me, after reading some articles, I discovered that the subject of "EMMIGRANTS" is not much investigated.

The final decision about studying (IMMIGRANTS or EMMIGRANTS or RETURNING EMMIGRANTS) DEPENDING ON other factors including HOUSING is still pending but all the data is now ready. The decision will be taken soon depending on some more litterature review.

The government of Canada promotes immigration as source of economic stability. However, although each year new permanent residents arrive to Canada, there are people leaving the country. In this study, I am trying to figure out if some internal reasons are pushing people to leave the country (EMMIGRANTS). Morever, I will investigate if these reasons (or factors) are pushing some people (RETURNING EMMIGRANTS) to come back to Canada.

So from Statistics Canada, I dowloaded the following data:

1. number of emmigrants, quarterly, from 1976 to 2022.
2. number of returning emmigrants, quarterly, from 1976 to 2022.
3. number of incidents (violations of law, crimes, etc..), yearly, from 1998 to 2022.
4. the measure of core inflation based on a weighted median approach, monthly, from 1990 to 2022.
5. the median income of individuals, yearly, from 1976 to 2022.
6. the unemployement rate of persons over 15, yearly, from 1990 to 2022.

The data was organized in excel and than imported (in total of 6 tables) to DB browser. Then, I used some of what I have learned from the SQL course to create new tables, join some tables on the column year, find the years for which the number of emmigrants and RETURNING EMMIGRANTS are maximum and minimum and many other queries..

Then, using scatter plot and correlation coefficient in python, I tried to understand the relation between the (EMMIGRANRTS and factors (income, inflation, unemployement, incident)) and (RETURNING EMMIGRANTS and factors.). It seems that the chance of having a linear relation between the dependent and independent variables is slim.

The next step would be to explore deeply the relations and use some statistical tools to draw conclusions.

The link to my video: https://1drv.ms/v/c/18aeda82e8cd97d3/EbpV8Fk2mzxErB3py_HvNWQBng6V5lCQ9P1qPID6vJWsIQ