Data Science Salary Visualizations - Canada 2025

A data visualization project analyzing salary trends across Canadian provinces for data science roles, helping students make informed co-op and career decisions.

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📊 Visualization Projects

1. Data Science Salaries Across Canada (2025)

Key Question: Where should data science students apply for co-op and entry-level positions to maximize salary potential?

Key Findings:

• 🥇 BC leads at $141k median salary (Vancouver tech hub)
• 🥈 Ontario follows at $129k (Toronto AI corridor - Vector Institute)
• 🥉 Alberta competitive at $114k (strong energy + tech data science market)
• 💰 Entry to senior range: $79k - $239k across Canada
• 📈 Data science premium: 10-20% higher pay vs software engineering

What makes this useful for students:

• Current 2025 data from Job Bank Canada, LinkedIn, and Glassdoor
• Compares median salaries AND complete salary ranges (entry-level to senior)
• Helps students set realistic salary expectations for co-op and negotiations
• Factors in cost of living considerations for relocation decisions
• Identifies geographic trade-offs for career planning

📂 Full Documentation: View Detailed Methodology

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� Features

📊 Interactive Dashboard

• Live filtering by province and salary type
• Cost-of-living adjustments for accurate purchasing power comparison
• Real-time visualizations with matplotlib integration
• Sortable data tables with gradient highlighting
• Student-focused insights and recommendations

📓 Jupyter Notebook Analysis

• Step-by-step data exploration with explanations
• Statistical analysis and key metrics
• Multiple visualizations comparing different perspectives
• Exportable results for further analysis

📈 Static Visualizations

• Original dual-panel salary comparison
• Cost-of-living adjusted comparisons
• Career growth potential analysis

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�🛠️ Technologies Used

• Python 3.13 - Primary programming language
• matplotlib & seaborn - Data visualization libraries
• pandas & numpy - Data manipulation and analysis
• Streamlit - Interactive web dashboard
• Jupyter - Data exploration notebooks

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🚀 Getting Started

Prerequisites

# Python 3.13 or higher
python --version

# pip package manager
pip --version

Installation

1. Clone this repository:

git clone https://github.com/aymanmomin/data-science-salaries-canada.git
cd data-science-salaries-canada

2. Create and activate virtual environment (recommended):

# On Windows
python -m venv .venv
.venv\Scripts\activate

# On macOS/Linux
python3 -m venv .venv
source .venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

Running the Project

🎯 Option 1: Interactive Dashboard (Recommended)

# Launch the Streamlit dashboard
streamlit run app.py

Open your browser to http://localhost:8501 to explore the interactive dashboard.

📓 Option 2: Jupyter Notebook Analysis

# Launch Jupyter
jupyter notebook

# Open notebooks/data_exploration.ipynb

Option 3: Generate Static Visualization

# Run the visualization script
python src/salary_analysis.py

The visualization will be generated as a PNG image in the same directory.

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📁 Project Structure

.
├── src/
│   └── salary_analysis.py                    # Python analysis script
├── notebooks/
│   └── data_exploration.ipynb                # Interactive data exploration
├── data/
│   └── salary_data.csv                       # Raw salary data
├── output/
│   ├── canada_data_science_salaries_2025.png # Original visualization
│   ├── salary_comparison_adjusted.png        # CoL-adjusted comparison
│   └── career_growth_analysis.png            # Growth potential chart
├── docs/
│   ├── index.md                              # GitHub Pages site
│   ├── methodology.md                        # Detailed methodology
│   └── _config.yml                           # Jekyll configuration
├── app.py                                    # Streamlit dashboard
├── requirements.txt                          # Python dependencies
├── .venv/                                    # Virtual environment (not in repo)
├── .gitignore                                # Git ignore rules
└── README.md                                 # This file

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📈 Data Sources

All visualizations use credible, up-to-date sources:

• Government of Canada Job Bank - Official labour market wage data
• LinkedIn Salary Insights - Industry compensation trends
• Glassdoor Canada - Company-reported salary information

Data accessed: November 2025 - January 2026
License: Open Government Licence - Canada (for Job Bank data)

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🎯 Skills Demonstrated

• Data Analysis: Processing salary data from multiple sources with cost-of-living adjustments
• Python Programming: Clean, efficient, well-documented code
• Data Visualization: Multiple chart types (bar charts, range plots, adjusted comparisons)
• Web Development: Interactive Streamlit dashboard deployment
• Statistical Analysis: Comparative analysis and purchasing power calculations
• Communication: Translating data insights into actionable career advice

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📝 License

This project is open source and available for educational and portfolio purposes.

Data sources maintain their original licenses (Open Government Licence - Canada for government data).

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👤 Author

Created by Ayman - Data Science Portfolio Project

Demonstrating practical data analysis and visualization skills for real-world career planning.

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🌐 Deployment Options

Run Locally (Quick Start)

# After installing dependencies
streamlit run app.py
# Open http://localhost:8501

Deploy to Streamlit Cloud (Free)

1. Push this repo to your GitHub
2. Go to share.streamlit.io
3. Connect your GitHub account
4. Select this repository and app.py as the main file
5. Click "Deploy"!
6. Get a free public URL to share on your resume

View Documentation

GitHub Pages: View Project Documentation →

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This is an independent project using publicly available data to demonstrate data science and Python visualization skills.