This repository contains practical examples, scripts, and notebooks demonstrating how to perform data analysis using Python. The focus is on extracting meaningful insights from raw data through cleaning, transformation, exploration, and visualization.
Data analysis is the backbone of decision-making in business, research, and technology. Using Python’s powerful ecosystem of libraries, this repository showcases how to:
- Load and clean datasets
- Explore and summarize data
- Visualize distributions, trends, and relationships
- Generate actionable insights
This repository is useful for:
- 📈 Business analytics and reporting
- 📚 Academic and research projects
- 📊 Data-driven dashboards and insights
- 🧾 Learning and practicing Python data analysis workflows
- Data cleaning and preprocessing
- Handling missing values and duplicates
- Descriptive statistics and correlation analysis
- Visualizations with Matplotlib, Seaborn, and Plotly
- Step-by-step Jupyter Notebooks for clarity
- Python 3.x
- Pandas – Data manipulation
- NumPy – Numerical computing
- Matplotlib – Visualization
- Seaborn – Statistical visualization
- Plotly – Interactive dashboards
This repository demonstrates how Python can transform raw data into valuable insights. By applying systematic analysis and visualization techniques, anyone can uncover patterns, identify trends, and make informed decisions.
Zohaib Sattar
📧 Email: Zohaib Sattar
🔗 LinkedIn: Zohaib Sattar
If you found this repository useful or insightful, please consider giving it a ⭐ on GitHub.
It motivates me to keep creating valuable open-source data science content and helps others discover this work. 🚀