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Data Analyst with less than a year in Python & SQL for business intelligence
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Final-year CSE undergraduate at KIIT with experience in Python, SQL, Power BI, Tableau, and statistical analysis. Built analytics projects across e-commerce, experimentation, aviation, startups, and sports domains using large-scale real-world datasets.
Kalinga Institute of Industrial Technology (KIIT)
B.Tech · Computer Science Engineering
August 1, 2022 – June 30, 2026
DAV Kapildev Public School
CBSE Class XII
June 1, 2019 – May 31, 2021
Flight Data Analytics Dashboard
June 21, 2026 – Present
Built a real-time analytics dashboard integrating live MySQL queries with Streamlit for airline and route analysis. Developed interactive Plotly visualizations and modular SQL query pipelines for dynamic business insights.
View ProjectIndian Startup Funding Analytics Dashboard
June 21, 2026 – Present
Built an analytics dashboard on 5,000+ startup records to uncover funding trends across sectors, cities, and investors. Performed data cleaning using regex normalization and entity resolution to merge duplicate startup and investor names. Developed KPI-driven visualizations for investor activity, funding distribution, and startup ecosystem analysis.
View ProjectE-Commerce Funnel Analysis
June 21, 2026 – Present
Analyzed 110M+ e-commerce events across 2 months using Python and Power BI to identify conversion bottlenecks in a multi-category store. Built a complete conversion funnel (View → Cart → Purchase) revealing only 5.04% overall conversion with an 89.2% drop-off at the View → Cart stage. Identified pre-holiday browsing behavior in November — View-to-Cart tripled (5.32% → 12.22%) while Cart-to-Purchase dropped 58%. Uncovered Apparel as the biggest opportunity — 209K viewers but only 0.97% conversion vs Electronics at 5.41%; Apple generated $24M revenue. Performed category-level and brand-level behavioral segmentation to identify high-value customer trends and revenue-driving products. Delivered a 4-page interactive Power BI dashboard covering funnel performance, monthly trends, category analysis, and customer behavior insights.
View ProjectA/B Testing Experiment Analysis
June 21, 2026 – Present
Designed and analyzed an end-to-end A/B experiment on a landing page using data from 290K+ users to evaluate conversion uplift between control and treatment groups. Performed power analysis, two-proportion z-tests, chi-square validation, and Bayesian Beta-Binomial inference to assess statistical significance. Found no statistically significant improvement in the new design (p = 0.19, 95% CI: -0.39pp to +0.08pp), recommending retention of the existing landing page. Built temporal conversion analysis pipelines to identify novelty effects and day-of-week behavioral trends using Python visualization libraries. Implemented data quality validation and mismatch removal pipelines to improve experiment reliability and consistency.
View ProjectIPL Analytics Web App
June 21, 2026 – Present
Built a 6-page Flask application with REST APIs processing 225K+ IPL deliveries across 950 matches. Improved API response efficiency by 45% using Flask-Caching and optimized data loading pipelines.
View ProjectIBM Data Science Specialization
Coursera
June 1, 2026 – Present
Supervised Machine Learning: Regression & Classification
DeepLearning.AI
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in diverse data analytics applications, from e-commerce and A/B testing to sports and startup funding. This breadth of interest aligns well with a dynamic data analyst role that requires adaptability and curiosity. The self-driven nature of these projects, coupled with certifications, suggests a proactive learning mindset. However, the lack of professional experience means cultural fit in a corporate setting is yet to be fully demonstrated.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude and an ability to translate complex data into actionable insights. The focus on identifying bottlenecks, evaluating experiments, and building interactive tools suggests a proactive and results-oriented approach. However, without direct work experience, it's difficult to assess operational fit in a team environment or stress handling capabilities.