This repository is part of my personal Data Science & Machine Learning journey.
Here, I’ve collected all my work, experiments, and progress on data visualization with Python.
Visualization is one of the most important steps in Data Science.
It allows us to:
- Understand the data
- Spot patterns, correlations, and outliers
- Communicate insights effectively
- Build intuition for the Machine Learning models that come later
This repo is not a tutorial — it’s my hands-on portfolio of work showing the progress I’ve made.
data-visualization-python/
│── README.md
│── requirements.txt
│── LICENSE
│── .gitignore
│
├── datasets/ # Sample datasets for practice
│ ├── sales.csv
│ ├── tips.csv
│ └── iris.csv
│
├── notebooks/
│ ├── 01-introduction.ipynb # Intro to Data Visualization, Why it matters
│ │
│ ├── matplotlib/ # Core Matplotlib Tutorials
│ │ ├── 02-matplotlib-basics.ipynb
│ │ ├── 03-line-bar-scatter.ipynb
│ │ ├── 04-histograms-boxplots.ipynb
│ │ ├── 05-customization-styles.ipynb
│ │ └── 06-subplots-layouts.ipynb
│ │
│ ├── seaborn/ # Core Seaborn Tutorials
│ │ ├── 07-seaborn-basics.ipynb
│ │ ├── 08-distribution-plots.ipynb
│ │ ├── 09-categorical-plots.ipynb
│ │ ├── 10-matrix-plots.ipynb # Heatmaps, pairplots, clustermaps
│ │ ├── 11-regression-plots.ipynb
│ │ └── 12-style-themes.ipynb
│ │
│ ├── advanced/ # Advanced visualization concepts
│ │ ├── 13-plotly-intro.ipynb # (Optional bonus interactive viz)
│ │ ├── 14-combined-viz.ipynb # Using Matplotlib + Seaborn together
│ │ └── 15-case-study.ipynb # Mini EDA visualization case study
│ │
Each notebook focuses on one step of visualization skills, starting from Matplotlib basics, moving through Seaborn, and finally to interactive Plotly visualizations.
- Matplotlib → Core plotting library in Python
- Seaborn → Statistical visualization built on top of Matplotlib
- Plotly → Interactive, dynamic, and modern plots
"A picture is worth a thousand words, but a visualization is worth a thousand data points."
- Data Cleaning & EDA: Visualization reveals mistakes, missing values, and distributions.
- Exploration: Helps build intuition about relationships between features.
- Communication: Turns raw numbers into stories decision-makers can understand.
- Machine Learning: Good visualizations help interpret and debug ML models.
This repository showcases the practical steps I’ve taken to reach a solid level in Data Science & Machine Learning.
It reflects real progress, not tutorials — serving as a portfolio of my journey.
💼 LinkedIn
🐙 GitHub
✉️ Email: abbaskhan0345060@gmail.com