Statistics Canada Employment Analysis
Statistics Canada
Employment Analysis
A data-analysis project built by a four-person team to explore
Statistics Canada employment data and compare full-time and
part-time teacher employment rates.
Turning raw data
into useful insight.
Statistics Canada Employment Analysis is a collaborative
data-analysis project focused on understanding teacher
employment patterns in Canada.
Our four-person team developed a program that processes
Statistics Canada employment data and compares full-time
and part-time teacher employment. The project combined
programming, data processing, statistical comparison,
and teamwork to transform raw public data into meaningful
information.
Working with
real-world data.
Unlike simplified classroom datasets, real-world datasets
can contain large amounts of information and require careful
organization before they can be analyzed.
Our challenge was to identify the relevant teacher employment
records, organize the information, separate full-time and
part-time employment categories, and perform meaningful
comparisons while maintaining consistency with the source data.
From dataset
to analysis.
Collect
Identify and obtain the relevant Statistics Canada
employment data.
Process
Organize and filter the dataset to isolate relevant
teacher employment information.
Analyze
Compare full-time and part-time employment figures
using programmed calculations.
Present
Transform the analysis into understandable results
and comparisons.
What I
worked on.
As one of four team members, I contributed to the
programming and data-analysis side of the project,
helping turn the raw dataset into a usable analysis.
- Processed and organized employment data
- Implemented data-processing logic
- Compared full-time and part-time employment
- Tested the program with different inputs
- Collaborated with teammates during development
- Helped integrate the final project
Tools behind
the analysis.
Beyond the
classroom.
This project gave me practical experience working with a
real-world dataset rather than a simplified programming
exercise.
I developed a stronger understanding of data cleaning,
filtering, processing, and comparison. I also learned how
important it is to understand the structure and meaning of
a dataset before writing calculations around it.
Working in a four-person team also strengthened my
communication, collaboration, debugging, and development
workflow.
Turning public data into
something people can understand
was the real goal of this project.