dikshya User

I’m a data analyst who turns raw, multi-source data into interactive Power BI dashboards and actionable business insights, especially with Python and SQL. I’ve built end-to-end analytics work that starts with data cleaning and modeling and ends with stakeholder-ready visualization.

On my Loan Default Analysis project, I cleaned and validated 255,000+ loan records in Python, then wrote 20+ SQL queries using CTEs, window functions, subqueries, and CASE statements. I also built a 3-page Power BI dashboard with custom DAX measures to highlight peak default risk factors for executives.

For Customer Behaviour Analysis, I ran EDA on 3.9k transaction records, cleaned data, binned ages, and connected directly via SQLAlchemy. I used MySQL queries with window functions, CTEs, and conditional aggregation to segment loyalty tiers and assess how discounts affect revenue, then surfaced insights in an interactive Power BI dashboard.

With Olist Dataset Analysis, I performed SQL analytics on 100k+ e-commerce orders—building retention cohorts, revenue MoM pipelines, and spend segmentation using NTILE. I cleaned datasets, modeled a 7-table star schema in Power BI with engineered dimension tables, and delivered an executive 3-page dashboard using custom DAX to visualize revenue and payment/product trends.