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 plotly

data-analysis-case-studies/
│
├── README.md
│
├── case-study/
│ ├── data/
│ │ └── files.csv
│ ├── codes/
│ │ ├── file.ipynb
│ │ └── code.py
│ ├── report.pdf
│ ├──presentation.pptx
│ └── README.md
│
└── case-study-2/

1. Clone the repository:

git clone <repository-url>

2. Navigate to a case study folder.

3. 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.