Back to Projects

Team Name:

Data Cupcakes


Team Members:


Evidence of Work

Predicting Pathways: Factors Influencing Young Australians Education and Employment

Project Info

Team Name


Data Cupcakes


Team Members


Harish , Niranjan , Puneeth , Shivam

Project Description


Education and Employment Outcomes in Australia

Project Overview

This project explores the factors that influence education, skills, and training choices of young people in Australia, specifically focusing on the impact of geography, funding sources, and education levels on their post-school study paths and employment outcomes. The analysis uses data from various sources including program enrollments, program completions, subject enrollments, and student demographics.

Objectives

  1. Geography's Role: Analyze how location (state/territory and remoteness region) influences students' educational and employment outcomes.
  2. Perception of Employment and Financial Outcomes: Examine young people's perceptions of employment and financial outcomes based on their education level and funding sources.
  3. Value of Education: Explore whether students perceive post-school education to be worth the money in both the short and long term.

Data Sources

The following datasets are used in this analysis:
- Program Enrollments: Contains information on student enrollments in different programs, categorized by location, education level, and other demographics.
- Program Completions: Tracks students who completed their enrolled programs.
- Subject Enrollments: Details on subject-level enrollments.
- Students: Includes student demographics such as age group, country of birth, highest school level completed, and labour force status.

Key Columns

  • State/territory of residence: Geographic location of students.
  • Remoteness region: Classification of areas as urban, regional, or remote.
  • Labour force status: Indicates whether students are employed, unemployed, or not in the labour force.
  • Apprentice/trainee status: Shows whether the student is an apprentice or trainee.
  • Level of education: Highest level of education attained.
  • Highest funding source: Funding received by the student (e.g., government-funded, self-funded).
  • SEIFA (IRSD): Socioeconomic index that measures financial disadvantage in the student’s area.
  • Total: Number of students in each category for aggregation purposes.

Analysis Breakdown

1. Does Geography/Location within Australia Play a Part?

Key Question: How do educational and employment outcomes vary across different states/territories and remoteness regions?

Approach:
- Created a stacked bar chart to visualize the distribution of Labour force status across different states/territories and remoteness regions.
- Used a choropleth map to visualize employment rates across geographic regions.

Outcome:
- Geographic differences in labour force outcomes were observed, with remote regions showing lower employment rates and higher levels of students not in the labour force.

2. What Are Young People's Thoughts on the Likely/Expected Employment and Financial Outcomes?

Key Question: How do students perceive employment and financial outcomes based on their education level and funding source?

Approach:
- Created a stacked bar chart to show the relationship between highest funding source and labour force status.
- Generated a box plot to explore the distribution of employment outcomes by education level.
- Used a scatter plot to examine the relationship between socioeconomic status (SEIFA) and employment outcomes.

Outcome:
- Students who were government-funded showed slightly higher employment rates.
- Employment outcomes varied significantly based on education level, with higher education levels generally correlating with better employment prospects.

3. Do Young People Perceive That Post-School Education Is Worth the Money?

Key Question: Is higher education seen as financially rewarding in terms of employment outcomes?

Approach:
- Generated a line chart to show how employment rates change based on level of education.
- Created a pie chart to represent the proportion of students in each education level who are employed, unemployed, or not in the labour force.

Outcome:
- Higher levels of education (e.g., Bachelor's degree, Master's degree) are associated with higher employment rates, suggesting that students with these degrees perceive education to be worth the investment.


#datacupcake #datavisualization #ai #educationchoices #youthtraining #careerpathways #vocationaltraining #highereducation #predictiveanalytics #dewrproject #datadriveninsights #workforcedevelopment #skillsforfuture #australia

Evidence of Work

Video

Homepage

Project Image

Team DataSets

The National Centre for Vocational Education Research (NCVER) - Data Builder

Data Set

Challenge Entries

Factors that influence education, skills and training choices of young people

What factors impact the decisions of young people to commence and complete post school studies (Vocational Education and Training or higher education), including those that commence and complete an apprenticeship?

#Study success: Choosing the right study paths

Eligibility: Open to participants including university students and professional researchers

Go to Challenge | 17 teams have entered this challenge.