I just completed a powerbi fintech dashboard project. I worked with a synthetic dataset from kaggle an I built a 2- pages interactive report analyzing customer behavior, account activity, and loan performance. I handled the data cleaning using powerquery
I just wrapped up a new project: Retail Sales, Customer Behavior & Promotion Analysis
This one dives into how retailers can boost revenue & optimize promotions by understanding customers, stores, and seasonal sales trends.
I just wrapped up a new project: Retail Sales, Customer Behavior & Promotion Analysis
This one dives into how retailers can boost revenue & optimize promotions by understanding customers, stores, and seasonal sales trends.
What excites me most about this project is how it connects data to real business value:
โ๏ธ Customer behavior insights
โ๏ธ Promotion effectiveness
โ๏ธ Store & seasonal performance
Not just dashboards but actionable strategies. ๐
Hiring isnโt just โapply โ interview โ hired.โ
Itโs a pipeline full of bottlenecks, mismatched expectations & hidden patterns.
I built a Recruitment Pipeline Dashboard (Excel + Power Query + DAX) to help HR teams see:
โ Whoโs in the talent pool
โ Where candidates get stuck
Hiring isnโt just โapply โ interview โ hired.โ
Itโs a pipeline full of bottlenecks, mismatched expectations & hidden patterns.
I built a Recruitment Pipeline Dashboard (Excel + Power Query + DAX) to help HR teams see:
โ Whoโs in the talent pool
โ Where candidates get stuck
โ How sourcing & salary align
The takeaway?
Recruitment data isnโt about counting applications. itโs about finding the story:
Why some roles attract interest but donโt convert
Why certain experience groups move faster
Why strong hires sometimes come from unexpected places
How do you handle the data modeling btwn a fact table with two important dates and the date table? That was my challenge for this analysis but I was able to overcome it through the active and inactive relationships and DAX's 'USERRELATIONSHIP()' function to activate the inactive