Himanshu Vyas

I've built end-to-end analytics solutions using SQL, Python, Power BI, and Excel to uncover business patterns and support data-driven decisions.

For Quantium's Data Analytics Virtual Experience, I analysed transaction-level retail data in Python to identify customer behaviour, basket trends, and revenue patterns. I also evaluated trial-store performance through control-group benchmarking and uplift analysis.

In my Consumer Complaint Intelligence & Early-Warning System, I processed and analysed 17M+ CFPB complaint records using Python and SQL Server. I applied chi-square tests, Cramér's V, NLP preprocessing, and Power BI reporting to surface complaint trends, issue drivers, and company-level insights.

I've also built a SQL Server Medallion Architecture data warehouse integrating CRM and ERP data, along with loan portfolio analytics across 50,000+ customer accounts. My work spans data cleaning, ETL, modelling, statistical analysis, KPI reporting, dashboards, and customer segmentation.