Explore an Interactive EV Population Dashboard with Power BI

Explore an Interactive EV Population Dashboard with Power BIExplore an Interactive EV Population Dashboard with Power BI

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Samuel Maina

• 21h

Electric Vehicle (EV) Population Dashboard

I designed and developed this interactive Power BI dashboard to analyze the growth, distribution, and market composition of over 150,000 electric vehicles.

Key Features & Insights Visualized:

Executive Metrics: Tracks 150.4K+ total electric vehicles across 37 manufacturers and 127 unique car models.

Interactive Filtering: Built dynamic slicers filtering data seamlessly by County, Model Year, and Vehicle Make.

Market Breakdown: Features an analysis of battery type distributions (BEV vs. PHEV), MSRP price brackets, and Clean Alternative Fuel Vehicle (CAFV) eligibility.

Trend & Geographic Analysis: Includes an inventory age growth curve tracking active EV adoption over time, paired with a geographic distribution map.

Power BI |Data Analytics |Data Visualization |Data Modeling

Data Analysis Data Modeling Data Visualization Microsoft Power BI

Ishita Guha Roy

• Jun 25

Data Cleaning & Feature Engineering Using Python

Python Data Analysis Data Engineering Web Scraping

Rizki Arsyad

pro

• Jun 24

New exploration: Email Marketing Analytics Dashboard

Wanted to rethink how email marketers interact with their campaign data. Most tools feel like spreadsheets with extra steps, so I explored a cleaner approach: scannable metrics, inline campaign comparisons, and a visual system that gives data-dense views room to breathe.

contra.com/p/rsesWlEW-email-marketing-analytics-dashboard-concept

Shahriar Jubayer Eshat

• Jun 25

clean

Liudmila Davydiuk

• Jun 24

Designed and built a full data pipeline — pulling from multiple sources, resolving inconsistencies, enriching with external variables, and delivering clean outputs through interactive Power BI dashboards stakeholders can actually act on. Designed and built a full data pipeline — pulling from multiple sources, resolving inconsistencies, enriching with external variables, and delivering clean outputs through interactive Power BI dashboards stakeholders can actually act on.

The starting point was a large, structurally complex dataset with inconsistent formats, missing values, and misaligned fields. The goal was a repeatable pipeline that transformed it into something reliable enough to drive real decisions.

The enrichment layer was the differentiator: layering in demographic and contextual variables from external sources to add depth that the raw data alone couldn't provide. The final output was a Power BI dashboard built for non-technical audiences — clear enough to act on, rigorous enough to hold up to scrutiny.

AI-assisted validation (Claude Enterprise) was used throughout to cross-check outputs and flag inconsistencies before delivery