Data-Driven Analyst · Storyteller with Numbers
Turning raw data into actionable insights. Passionate about HR Analytics, MIS, and Data Visualization. Currently pursuing Diploma in Data Science from IIT Madras.
I'm Namra Sania — a data-obsessed analyst with a knack for uncovering patterns that drive business decisions. My work bridges the gap between raw data and strategic action.
With hands-on experience in Excel, SQL, Power BI, Python and ML,Built dashboards that help HR leaders reduce attrition, analyzed employee wellbeing across 3,000+ records, and even helped a vegetable business boost profit margins by 35%,
Currently pursuing a Diploma in Data Science from IIT Madras, I'm constantly learning and applying new techniques to solve real-world problems. I'm actively seeking HR Analytics, MIS, or Data Analyst roles where I can make an impact.
Analyzed attrition patterns across 1,470 employee records. Built KPI metrics including attrition rate, average salary, and department-wise turnover using DAX calculated measures.
Cleaned and transformed raw HR data using Power Query; identified salary-band attrition patterns that provided actionable retention strategy recommendations.
Full-cycle analytics project on 3,000 employee records: EDA in Python (Pandas), data cleaning in Excel, and SQL querying with 6 derived calculated columns (age groups, sleep quality, risk scores).
Visualised insights in Power BI with KPI cards and heatmaps; delivered a structured, analysis-ready model relevant to employee wellbeing and engagement tracking.
Collected and cleaned 31 days of real primary data; tracked revenue, cost, and profit across 16 product lines and 6 credit customers (₹1.5L+ transactions).
Applied analysis to identify a 35% margin yielding ~₹1,419 daily profit; flagged high-risk credit exposure and delivered a formatted, recommendation-driven report to business owner.
Analysed revenue, profit, quantity, and AOV across transactional data; built dynamic dashboards with time, geography, and payment-mode filters for business-decision support.
AFRA's sales and purchase records were scattered, with no real visibility into profitability. Organized and analyzed 5 months of data in Excel — 1,650 sales line items, 295 customer bills, and 206 purchase records — to track gross profit, product performance, and category margins
Applied analysis to identify a ₹17.44Lin sales 44.7% overall gross margin, and a sharp profitability gap by category: Furnishing Accessories ran a 72.1% margin against just 35.6% for Bedding — giving the business a basis for smarter purchasing and pricing decisions.
I'm always open to new opportunities, collaborations, or just a friendly chat about data!