A curated repository focused on strengthening data fundamentals and progressively scaling toward real-world analytics, data science, and engineering projects.
This space is not about volume, but about intentional practice, clear reasoning, and continuous improvement.
This repository serves as a personal learning and portfolio space where I:
- Practice core SQL and Python concepts through selected coding challenges
- Document reasoning, assumptions, and analytical thinking
- Transition gradually from platform-based exercises to real data projects, including:
- Data manipulation and transformation workflows
- Analytical case studies
- Introductory Machine Learning models
What you will find here:
- Carefully selected challenges with strong learning value
- Clear, readable, and well-documented solutions
- Emphasis on correctness, edge cases, and analytical thinking
What you will NOT find here:
- Massive dumps of solved problems
- Copy-paste or auto-generated solutions
- Speed-focused or context-free answers
Quality over quantity. ALWAYS.
- SQL (MS SQL Server dialect)
- Python
- Git & GitHub
(Future extensions may include Jupyter Notebooks, BI tools, and cloud-based data platforms like Snowflake or Databricks)
.
├── sql/hackerrank/ 31 solutions in T-SQL (15 easy, 14 medium, 2 hard)
├── python/hackerrank/ 7 solutions (6 easy, 1 medium)
├── templates/ solution templates for SQL and Python
├── CONTRIBUTING.md
└── README.md
.
- Consistent formatting and naming conventions
- Clear separation between problem context, approach, and solution
- Readability prioritized over cleverness
This repository evolves as my skills evolve:
- Early sections focus on fundamentals and structured problem-solving.
- Later sections aim to reflect real-world data analysis, science, and engineering workflows.
Learning is iterative; this repo documents the journey.
If you reached this far, THANK YOU! Feel free to explore the SQL & Python challenges and project cases.