📊 Course: IBM Data Visualization (Coursera)
🛠 Skills Learned: Matplotlib, Seaborn, Plotly, Dash, Graph Skeletons, and more
This repository contains my work from the IBM Data Visualization Course on Coursera. Throughout this course, I gained hands-on experience with various data visualization tools and techniques, including:
- Matplotlib & Seaborn (static and statistical plots)
- Plotly & Dash (interactive visualizations & web apps)
- Graph Skeletons (structuring effective visualizations)
- Best Practices in data storytelling
Each lab and assignment helped me understand how to choose the right plot, customize visuals, and present data effectively.
| Tool/Library | Purpose |
|---|---|
| Matplotlib | Basic plotting (line, bar, scatter, histograms) |
| Seaborn | Statistical visualizations (heatmaps, boxplots, violin plots) |
| Plotly | Interactive plots (3D charts, animations, hover effects) |
| Dash | Building web-based dashboards |
| Pandas | Data manipulation & cleaning |
| NumPy | Numerical computations |
├── modules/ # Lab exercises (Jupyter notebooks)
│ ├── module1/ # Basic plots (line, bar, scatter) and exploring data visualization
│ ├── module2/ # Area plots (understanding area plots)
│ ├── module3/ # Waffle word clouds
│ ├── module4/ # Plotly (removed due to size issue)
│ └── module5/ # Final assignments (plotting and building a dash app)
│
├── notion_notes/ # My notion notes
├── certificate/ # Coursera certificate
└── README.md # This file
-
Clone the repo
git clone https://github.com/SamedyHUNX/data-visualization cd data-visualization -
Install dependencies
pip install -r requirements.txt # If applicable -
Run Jupyter notebooks
jupyter notebook
-
For Dash apps
python app.py # If applicable
✅ Choosing the right plot for different data types
✅ Customizing visuals (colors, labels, annotations)
✅ Interactive dashboards with Plotly & Dash
✅ Data storytelling best practices
This project is part of an IBM/Coursera course and is intended for educational purposes.
✉️ Email: samedy.hunx@gmail.com
Happy visualizing! 🎨📈