A beginner-friendly yet industry-aligned collection of data analysis case studies.
This repository focuses on data preprocessing, cleaning, visualization, and insight generation using datasets from official and reliable sources.
Each case study includes:
- Cleaned datasets
- Python/Jupyter analysis
- Visualizations
- A detailed written report (PDF)
- A presentation (PPT) for non-technical stakeholders
Python Version: 3.11
Libraries Used:
- pandas
- numpy
- matplotlib
- seaborn
- plotly
pip install pandas numpy matplotlib seaborn plotlydata-analysis-case-studies/
│
├── README.md
│
├── case-study/
│ ├── data/
│ │ └── files.csv
│ ├── codes/
│ │ ├── file.ipynb
│ │ └── code.py
│ ├── report.pdf
│ ├──presentation.pptx
│ └── README.md
│
└── case-study-2/
- Clone the repository:
git clone <repository-url>-
Navigate to a case study folder.
-
You can:
- Read the PDF report for a complete narrative
- View the PPT for summarized insights
- Explore the Jupyter Notebook / Python files to understand:
- Data cleaning decisions
- Visualization logic
- Insight generation process
No prior advanced ML knowledge is required.
By exploring these case studies, you will learn:
- How to clean real-world datasets
- How to perform exploratory data analysis (EDA)
- How to visualize trends and patterns
- How to translate analysis into clear written insights
- How to present data for both technical and non-technical audiences
- Add more real-world case studies
- Improve visual consistency across reports
- Introduce ML-based analysis where appropriate
- Add summary dashboards
Created by Keshav Pal, for learning, practice, and portfolio building in Data Analysis and Data Science.
⭐ If you find this repository useful, feel free to explore, learn, and adapt it for your own projects.