I’m a data analyst focused on operations, inventory, and data accuracy. I have hands-on experience working with POS systems and structured datasets, where I’ve maintained product records, resolved discrepancies, and ensured data remains reliable across systems.
I’m especially interested in how data supports real-world operations. My work focuses on identifying inconsistencies, improving data quality, and helping systems reflect what’s actually happening in the real world.
I specialize in Excel and Power BI for operational reporting, dashboard development, and data visualization, with experience using SQL for data cleaning and analysis.
⭐Data Visualization (Power BI, Excel)
⭐ Excel
⭐ Catalog & Inventory Management
⭐ Attention to Detail
⭐SQL (Data Cleaning & Querying)
⭐ Problem Solving
Developed a three-page interactive Power BI dashboard analyzing retail sales, inventory management, and product performance. The project demonstrates data modeling, Power Query transformations, DAX measures, KPI development, and business intelligence reporting using a retail sales dataset from Kaggle.
https://github.com/CoreyS-code/Retail-Sales-Inventory-PowerBI-Dashboard
Developed an interactive Power BI dashboard analyzing 500,000+ Philadelphia 311 service requests to evaluate service demand, departmental workload, request resolution, and operational trends.
Created KPI cards, DAX measures, interactive filters, and visualizations to identify high-volume request categories, monitor service performance, and communicate operational insights from a large public dataset.
https://github.com/CoreyS-code/Philadelphia-311-PowerBI-Dashboard
Built an interactive Power BI dashboard using the Palmer Penguin dataset to visualize the relationship between flipper length and body mass.
Included a regression plot with a trendline and RMSE (392 g), a body-mass distribution histogram, and an average-mass-by-species bar chart to highlight species-specific patterns and the model’s strong predictive accuracy.
Analyzed a dataset of beer styles to explore patterns in alcohol content (ABV) and bitterness (IBU), performing data cleaning, organization, and analysis in Excel. Structured the dataset using pivot tables and summary statistics to examine how strength and bitterness vary across beer categories.
Developed visualizations including bar charts, histograms, and a scatter plot to highlight distribution patterns and relationships between variables. The analysis revealed that most beers cluster within moderate ranges (4–7% ABV and 20–70 IBU), while higher ABV beers tend to exhibit increased bitterness. Outliers, such as Imperial Stouts and Barleywines, demonstrated how specialty styles push beyond typical ranges.
This project analyzes DoorDash delivery data containing over 19,000 records to identify the primary factors affecting delivery times across the order lifecycle. The objective was to determine whether delays were driven by restaurant preparation time or driver delivery performance.
I began by cleaning and structuring raw timestamp data, then calculated key operational metrics including total delivery time, restaurant preparation time, and driver delivery time. Using pivot tables and data visualizations, I compared ASAP and scheduled orders to uncover timing patterns and performance differences.
The analysis revealed that restaurant preparation time is the primary driver of delays, while driver delivery time remains relatively consistent across order types. Scheduled orders showed significantly longer total delivery times due to built-in waiting periods before preparation begins.
Based on these findings, I recommended improving restaurant preparation timing, enhancing communication around customer delivery windows, and monitoring restaurant performance more closely to reduce delays and improve operational efficiency.
[DoorDash Project.pdf](<https://drive.google.com/file/d/1F0g324PyHtcbylFOC3QRlnmUQdoQNhwj/view?usp=sharing>)
**Thanks!**
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