SQL-based EV charging analysis with revenue insights and visualization
This project analyzes electric vehicle (EV) charging sessions using SQL to explore usage patterns, charger performance, and revenue across different cities. The goal is to practice SQL for data analysis while combining it with Excel for reporting and visualization.
Instead of using Python, all visualizations in this project were created in Microsoft Excel, using pivot-style summary tables and charts. This demonstrates practical skills in both SQL (for analysis) and Excel (for business-focused reporting), which are essential tools for entry-level data analyst roles.
- SQL analysis of EV charging session data
- Revenue, usage pattern, and peak-hour calculations
- Visualizations created entirely in Microsoft Excel
- Pivot-style summary tables for business insights
- Clean, professional GitHub project structure
All project visualizations were created using Microsoft Excel Pivot-style charts and exported as PNG images for display.
A full Excel workbook containing raw data, pivot summaries, and charts is available in theexcel/folder.
A complete Excel workbook containing pivot-style tables and the original charts used in this project is available here:
- DC Fast chargers generate higher revenue per session
- Evening hours show higher energy usage
- Urban stations outperform lower traffic locations
To explore the SQL and results yourself:
- Clone this repository:
git clone https://github.com/ijeomaoku/ev-nest-sql-analysis.git
- Open the sql/ folder and run ev_nest_project.sql in your SQL environment (PostgreSQL, MySQL Workbench, DBeaver, etc.).
- Execute the analysis queries included in the script.
- SQL (DDL, DML, Aggregations)
- Data Analysis & Business Intelligence
- Visualization using Mircosoft Excel
- Built SQL-based data analysis project analyzing EV charging demand, pricing, and revenue using simulated operational data for a startup charging network.
- Add Python ETL pipeline to automate data ingestion
- Integrate Power BI dashboard for interactive reporting
- Expand dataset to include seasonal and geographic trends
- Predictive modeling for demand forecasting