Turning raw data into decisions recruiters can trust.
I'm Sonu Meena — a final-year MCA student and aspiring Data Analyst who builds clean, decision-ready analytics with Python, SQL, and Power BI. Two internships and three end-to-end projects in, I'm looking for the role where that keeps compounding.
Background
I'm currently in my final year of an MCA at the University of Kota (Dept. of Computer Science and Informatics), building on a B.Sc. in Computer Science from the University of Delhi. Along the way I've worked inside two analytics teams — The Kobalt and the Tata iQ × Forage GenAI Data Analytics program — cleaning real datasets, running EDA, and shipping reports that hold up to scrutiny. Outside of internships, I build my own projects end-to-end: from raw CSVs to dashboards a non-technical stakeholder could actually use. I care about the boring parts — clean joins, sane null-handling, a chart that doesn't mislead — because that's what makes the interesting parts trustworthy.
Where I've worked
45 days
- Applied Python and SQL to clean, explore, and analyze operational datasets for business reporting.
- Delivered analysis and visualizations used to inform team-level decisions during the internship.
Virtual experience
- Completed the GenAI-Powered Data Analytics program, simulating an engagement for Geldium Finance to reduce credit card delinquency.
- Ran EDA on a customer financial dataset — missing data patterns, class imbalance — and delivered a formatted EDA Summary Report with modeling recommendations.
Toolset
LANGUAGES & ANALYSIS
VISUALIZATION & BI
Selected work
Three projects, each taken from raw data to a stakeholder-ready output.
Analyzed 50,000+ rows of e-commerce transactions to track revenue, profit margin, and customer behavior by region and category. Wrote SQL to extract and aggregate data from a relational database, then built an interactive dashboard with KPI cards, slicers, and drill-downs for stakeholder decision-making.
Ran end-to-end EDA across 16 seasons of IPL match data to surface batting, bowling, and team performance trends. Identified top run-scorers, wicket-takers, and winning patterns using groupby aggregations, then built 10+ visualizations to make the findings accessible to non-technical audiences.
Analyzed food surplus and redistribution data from F&B establishments and charitable organizations to quantify waste-reduction impact, identifying peak waste periods and high-impact donor–charity pairings. Findings supported platform decisions aimed at reducing food insecurity while minimizing unnecessary energy consumption.