A comprehensive, beginner‑friendly yet industry‑relevant masterclass repository focused on data analysis, exploratory data analysis (EDA), and powerful data visualizations using Python. This repository is designed to act as a ready‑to‑go reference for real‑world data science and analytics projects.
This project covers the complete data visualization workflow using Python — from basic programming concepts to advanced, interactive visualizations and real‑world dataset analysis.
You will learn how to:
- Write clean and effective Python code
- Manipulate and analyze data using NumPy and Pandas
- Perform Exploratory Data Analysis (EDA) on real datasets
- Create static and interactive visualizations
- Communicate insights effectively through charts and dashboards
This repository can be used for:
- Learning data visualization from scratch
- Practicing EDA for interviews and projects
- Reference material for academic and professional work
- Python fundamentals for data analysis
- Working with data structures
- File handling and data loading
- Arrays and vectorized operations
- Mathematical and statistical functions
- Performance‑optimized data processing
- DataFrames and Series
- Data cleaning and preprocessing
- Handling missing values
- GroupBy operations
- Reading and writing data (CSV, Excel, etc.)
- Matplotlib – foundational plotting
- Seaborn – statistical visualizations
- Plotly & Cufflinks – interactive plots
- Pandas built‑in visualization tools
Hands‑on EDA is performed on multiple real‑world datasets, including:
- 🏠 Boston Housing Dataset
- 🚢 Titanic Dataset
- 🦠 Latest COVID‑19 Dataset
- 🏏 IPL Cricket Matches Data
- ⚽ FIFA World Cup Matches Data
- 📝 Text Data EDA
Each dataset includes:
- Data understanding
- Data cleaning
- Statistical analysis
- Insight‑driven visualizations
- Bar Charts
- Line Charts
- Stacked Charts
- Pie Charts
- Histograms
- KDE Plots
- Box Plots
- Violin Plots
- Auto‑Correlation Plots
- Interactive Dashboards
All charts are customized using:
- Colors
- Fonts
- Line styles
- Layouts
- Complete EDA on COVID‑19 data
- Kaggle‑style EDA on Boston Housing & Titanic datasets
- Sports data analysis and visualization
- Interactive visualizations using Plotly
- Data manipulation using NumPy & Pandas
- Visualization best practices
- How to convey more insights using fewer visuals
- Installation and setup of Python & libraries
- Computer (Windows / macOS / Linux)
- Internet connection
- Curiosity to learn 🚀
pip install numpy pandas matplotlib seaborn plotly cufflinks- Beginners in Python programming
- Beginners in Data Science & Machine Learning
- Students preparing academic or final‑year projects
- Developers working in analytics & visualization
- Anyone curious about data‑driven decision making
- Professionals wanting strong EDA fundamentals
No prior Python experience is required.
- Python basics
- NumPy practice notebooks
- Pandas data analysis notebooks
- Visualization notebooks
- Dataset‑specific EDA notebooks
- Interactive visualization examples
✔ Learn by working on real datasets
✔ Industry‑oriented visualization techniques
✔ Beginner to intermediate progression
✔ Strong foundation for machine learning projects
✔ Reusable code for future analytics work
👉 Repository Link: https://github.com/udityamerit/Complete-Data-Visualization-in-Python
Contributions, suggestions, and improvements are welcome. Feel free to:
- Fork the repository
- Raise issues
- Submit pull requests
This project is open‑source and intended for educational purposes.
Uditya Narayan Tiwari B.Tech – Computer Science & Engineering (AI & ML)
🔗 Portfolio: https://udityanarayantiwari.netlify.app/
🔗 GitHub: https://github.com/udityamerit
🔗 LinkedIn: https://www.linkedin.com/in/uditya-narayan-tiwari-562332289/
🔗 Knowledge Base: https://udityaknowledgebase.netlify.app/
Happy Learning & Visualizing 📊✨