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I got to practice Excel, SQL, and Tableau (and make a cool dashboard) in this fascinating crime data analytics project!
I helped a Supply Chain company solve business problems by making an interactive dashboard in Power BI!
The statistical power of R is on full display in this People Analytics project.
Check out my foray into the data analytics side of Python using real-life iron refinement plant data.
My love for sports and Tableau come together in this basketball analytics project, give it a look!
Read this project to find out about my deep dive into hospital data using more of SQL’s functions and capabilities.
In this project, I utilized SQL to uncover aggregations from almost 1.5 million rows as I evaluated Jordan’s standing with the World Bank.
I analyzed over 2000 rows of food delivery data using Microsoft Excel. I practiced Pivot tables, pivot charts, aggregation functions, and cleaning techniques among other things. I made the write-up in a LinkedIn article.
In this case study from Data Analytics Accelerator, I was prompted to analyze the State of Massachusetts education data. The main focuses were: What schools are struggling the most? How does class size affect college admission? What are the top math schools in the state? The link to the dashboard itself can be found here.
Winning a game is better than losing, and sometimes we can use math to help us find out how much we can expect to win. Read this project to see the power of mathematics to help us make predictions and find patterns in the world around us, including games.
Stephanie Atherton and I collaborated on this NSF-funded research project in summer 2024! We focused on the following research question: given the holonomy group of the cube on a surface made of squares, what subgroups can we obtain by varying the surface? We used abstract algebra and algebraic topology to find out.
My senior year at Texas Tech, I took a mathematical modeling class in which each group used real-world, local data to create feral cat population models. In this project, I worked with Oladepo Gbemisola and Ryan Covington to produce equations and predictions based on the Trap, Neuter, and Return rates adopted by local shelters. My focus was on researching reasonable equation parameters and cleaning/visualizing over 8000 rows of local feral cat data in Microsoft Excel. The dataset can be found here. The final Google Slides presentation can be found here.