Data Science Executive Week 2025

1:00pm CDT N201ABC
Capstone OL-Team1
Title: Wind Wise Solutions: Predicting Capacity and Longevity of Wind Turbines
Project Description: The impending depletion of non-renewable energy resources threatens U.S. energy security, making the transition to renewable sources urgent; however, optimizing wind energy locations demands a delicate balance to maximizing production and sustainability. Wind Wise Solutions aims to predict wind turbine capacity and estimate the years until retrofitting is required due to physical damage for given additional locations across the contiguous United States, supporting the expansion of wind energy installations. The models will incorporate data from wind installations spanning from 2001 to 2020, along with environmental variables, ecosystem factors, and regulatory and financial constraints, to generate accurate predictions for new installations. This predictive approach will enhance operational efficiency and contribute to the long-term sustainability of wind energy infrastructure.
Master Students: Bryce Tognozzi, Catherine Brockert, Jordan Domenick
1:30pm CDT N201ABC
Capstone OC4 – Team4
Title:  Analyzing Demographic Disparities in Columbia, MO, Police Department Traffic Stop Data
Project Description: This study examines the infamous issue of how demographic factors such as age, gender, and geographic location influence the likelihood of being stopped by law enforcement in Columbia PD. By combining block level census data with 911 traffic stop records, we aim to identify patterns that could suggest whether certain groups of people are more likely to experience traffic stops. The study seeks to explore whether factors like time of day, location, or major events (such as sports games or public protests) have an impact on traffic stop rates. Our approach involves using geospatial mapping techniques to visualize traffic stop trends and applying statistical analysis to determine if there are any significant correlations between demographic groups and traffic stop frequency. Additionally, we will explore how local police department practices, such as shift changes or law enforcement policies, might contribute to these patterns. Ultimately, the goal of this research is to provide data science driven insights that can inform local traffic enforcement policies, improve transparency, and promote fairness within the community.
Master Students: Syed Ali Hashmi, Karan Karthik, Deshan Wattegama