Mathematics graduate with hands-on experience in data analysis across healthcare, finance, telecommunications, and e-commerce. Skilled in Excel, Power BI, SQL, and Python — with a portfolio of 4 completed projects analyzing datasets of up to 130,000+ records. Passionate about turning raw data into clear business insights that drive smarter decisions.
Bachelor of Science (B.Sc.)
Relevant Coursework: Data Analysis & Computation, Statistical Methods, Business Mathematics
Tool: Microsoft Excel | Domain: Healthcare
Analyzed 10,000 healthcare claims to identify fraud patterns across providers and insurance types Built an interactive Excel dashboard with 4 charts, 6 KPI cards and 4 slicers for dynamic filtering Key finding: Fraudulent claims averaged $990 vs $535 for legitimate claims — an 85% difference, indicating inflated billing as the primary fraud tactic General Practice providers showed the highest fraud rate at 9.69% across all specialties
Tool: Power BI | Domain: Financial Services
Analyzed 130,138 loan records to identify key drivers of default risk across DTI, income, credit score, and loan size Built an interactive Power BI dashboard revealing counterintuitive insight: small loans default at 37% vs 22% for large loans Key finding: High income borrowers had the highest default rate at 40%, proving income alone is an unreliable risk predictor
Tool: SQL (Microsoft SQL Server) | Domain: Telecommunications
Wrote 9 SQL queries analyzing 1,000 telecom customers to identify churn drivers and high-risk customer profiles Key finding: Month-to-Month + Fiber Optic + No Tech Support customers churned at 57% — over twice the overall churn rate Identified 3 complaints as the critical tipping point — beyond which churn rate jumps from 25% to 36%
Tool: Python (Pandas, Matplotlib, Seaborn) | Domain: E-Commerce
Analyzed 128,975 real Amazon India orders to uncover revenue trends, category performance, and regional insights Key finding: Sets and Kurtas account for 77% of 78.6M total revenue, representing significant category concentration risk Identified B2B customers as an untapped opportunity — higher average order value (₹701 vs ₹648) but less than 1% of total revenue
Completed
Self-directed learning with applied project
Promoted financial literacy and investment awareness among students
Community engagement in data and analytics education
Active interest in blockchain technology and decentralized finance