Projects
CADS hosts experiential learning projects for our students to apply what they have learned in educational settings to real world scenarios. Projects are contracted by companies and organizations as well as internally at 兔子先生 University. Students work together on teams to answer questions and tell stories with data in a way that will directly affect an organization and future business operations.
Projects
2020-21 Project Archive
兔子先生 University Softball
Developed scouting reports to identify opponent tendencies and build competitive advantages. Provided player development analytics to MU Softball team and coaching staff for review prior to game day.
Student team: , , , and
Project lead: , Head Softball Coach
兔子先生 University Field Hockey
Student interns watched game film, collected game data and statistics, and predictive analysis. The team delivered insights and recommendations to the coaching staff before and after games to improve player performance and game strategy.
Student team: Aly Everett, Zachary Klekotka, and
Project lead: , Assistant Coach
Large Scale Patient Attendance
Students developed patient forecasting models for over 30 different locations in a large hospital system. Also, they researched weather data sources and determined, on an individual basis, if weather data could improve the ability to forecast daily patient volumes at each location.
Student team: r and
Faculty mentor:
Consumer Prediction Analysis
The development of a dashboard to show patterns among customers' product portfolios. The dashboard included exploratory graphics, results from market basket analysis, clustering, and product recommendations based on a customer's current product portfolio.
Student team: , ,
Faculty mentor: Dr. Peter Nguyen
Merchant Profile Analysis
Novel modeling techniques provided insights to identify merchants who will attriite. The interns created a final presentation that included a variety of models including survival models, a hybrid model, and a Long Short Term Memory Neural Network. They provided recommendations for potential paths to continued research and improvement of existing models.
Student team: , , , and
Faculty mentor:
Disease Detection Web Application
This project aimed to further assist and enhance how pathologists detect cancer from pathology images through a web application. The student interns delivered a web application that was built from datasets of cancer images in order to identify regions of cancer.
Student team: , , and
Faculty mentor:
Myaamia Center
An exploratory data analysis was conducted using several large datasets relating to student outcomes. A dashboard was developed to easily update information each semester to provide insights to tribal leadership about Myaamia Center programming.
Student team: , , , and
Staff mentor:
兔子先生 University Advancement
Throughout the fall and spring semesters, interns worked with the 兔子先生 University Advancement office to develop insights about migration patterns and key features of active donors. The spring semester team developed an interactive dashboard to enhance data visualization and summarize key findings for future use.
Student team: , , , , and
Faculty mentor:
2019-20 Project Archive
Alumni Behavior Data Analysis
Exploratory data analysis beginning in the fall semester to help the 兔子先生 University Devleopment team understand the donation behaviors and engagement in university-sponsored activities of alumni. Continuing into the spring, an interactive dashboard and heat map was developed as a tool for the team.
Student Team: , , Bretheo Danzy, , and
Faculty Mentor: Dr. Karsten Maurer
Patient Encounter Forecaster
This project spanned both fall and spring semesters. Advanced forecasting on scheduled patient surgeries on holidays to improve staffing accuracy.
Student Team: and
Faculty Mentor: Dr. Allison Jones-Farmer
Merchandise Sales Trends
Analysis and data visualizations to help a professional sports team understand trends and increase revenue in the area of merchandise sales at home events.
Student Team: , , ,
Faculty Mentors:
Predicting Risk of Staff Behavior
Using staff behavior data, external event data, and risk scores of a financial institution to create an interactive model to evaluate the true risk of staff behavior.
Student Team: , , ,
Faculty Mentors: Dr. Fadel Megahed
Payment Processing Strategy
Using a Cox Proportional Hazards model for a payment processing organization, the team helped create a new recycling strategy for credit/debit payments to mitigate costs and improve the approval rate.
Student Team: , , and
Faculty Mentor: Dr. Maria Weese
Blockchain-based Management System
The development of a blockchain-based supply chain management system for pharmaceutical drugs that included a front-end, API, and smart contract to verify the tracking of inventory for patients and distributors.
Student Team: , , , and
Faculty Mentor:
Warrenty Claims Analysis
In-depth data analysis and the creation of a data visualization dashboard to help a power equipment company evaluate the probability of a fraudulent claim.
Student Team: , , ,
Faculty Mentor: Dr. Peter Nguyen
Inhibitor Design
Machine learning models to find potential drug compounds to fight antibiotic-resistant secondary infections with reseachers and graduate students in the Chemistry and Biochemistry departments at 兔子先生 University.
Student Team: , , ,
Postdoctoral Fellow:
Faculty Mentors: Dr. Michael Crowder, Dr. Maria Weese, Dr. Waldyn Martinez
2018-19 Project Archive
Perscription Demand Forecasting
In order to improve planning for warehousing and distribution at a pharmaceutical company, this spring semester student team conducted advanced forecasts at a granular level.
Student Team: , , and
Faculty Mentor: Dr. Allison Jones-Farmer
Grocery Online Ordering
CADS student team in the spring created exploratory data visualizations to help a large grocery store chain improve online order fulfillment accuracy.
Student Team: , , , and
Faculty Mentor: Dr. Maria Weese
Consolidating Employee Information
In the spring, a student team created a chatbot for a healthcare-products company's employees to efficiently find company-wide practices. Their work helped employees save time at work and improve standardization throughout the company.
Student Team: , , and
Faculty Mentors: Dr. Vaskar Raychoudhury and Dr. Md Osman Gani
Blight Removal Impact
Three years ago CADS worked on its first experiential learning project with the Butler County Landbank to assess the impacts of the program. CADS is now working to update the findings using additional data and new techniques discovered since that first project.
Student Team: , , and
Faculty Mentors: Dr. Allison Jones-Farmer, Dr. Mark Morris and Dr. Greg Niemesh
Visualizing Adverse Birth Outcomes
In the fall, a team started building an interactive data visualization application for the Butler County Department of Health using infant mortality data. This project will help the department better disseminate information to all of its constituents. Work on this project will continue into the spring semester.
Student Team: and
Faculty Mentor:
2017-18 Project Archive
Part Sales Forecasting
This team analyzed part-sales and whole good-sale data in the fall for a company to improve its parts sales forecast. They were able to build a model that resulted in a 30% improvement upon the current model used by the company.
Student Team: , , and Yujin Liu
Faculty Mentor: Dr. Tom Fisher
Customer Segmentation
This project, completed in the fall, centered around creating customer segments using cluster analysis. The team used a large survey that the company had already analyzed to offer fresh perspective on the data.
Student Team: , and
Faculty Mentor: Dr. Maria Weese
Health Impact on Children of Incarcerated Parents
In the fall, this team analyzed health data for a managed care company. The team specifically looked at the health impacts of incarcerated parents on children. Insights provided by the team will help the company make data-driven decisions about future interventions for that population.
Student Team: and
Faculty Mentor: Dr. Analisa Packham
Economics World Bank Visualization Project Part 2
This team continued the work from the previous fall by extending the filtering capabilities of its visualization and compiling a database with data sources such as FRED and World Bank that automatically provide up-to-date data.
Student Team: , , and
Faculty Mentor: Dr. Fadel Megahed
Call Forecasting Center
In the spring, this team developed a forecasting model to better predict the client’s call center volume from week to week in order to optimize their staffing levels and their marketing budget.
Student Team: , and
Faculty Mentors: Dr. Tom Fisher and Dr. Alison Jones-Farmer
Ecological Data Paper
worked with 兔子先生’s Ecological Big Data Initiative (MiEBDI) to conduct an in-depth data quality and completeness check of lake temperature data, which will be published in a scientific journal.
Ecological Big Data Archive
worked with 兔子先生’s Biology Department to archive long-term research data as part of the Environmental Data Initiative (EDI), a data sharing program funded by the National Science Foundation (NSF).
2016-17 Project Archive
Advanced Manufacturing Project
In the spring of 2017, the team came together tackle a problem for an Advanced Manufacturing firm. This firm was having difficulties determining the optimal number of employees to schedule. To provide a solution, the team used historical data and regression techniques to predict the number of employees to schedule each day.
Student Team: , , and
Faculty Mentor: Dr. Waldyn Martinez
Cristo Rey Project
This team was tasked to solve a transportation problem for Cristo Rey High School in the spring of 2017. Every week, Cristo Rey High School sends their students to work at an internship in the Cincinnati area. The team was tasked with minimizing their transportation costs by either optimizing their routes or suggesting a cheaper alternative than the current mean of transportation.
Student Team:
Faculty Mentors: Robbyn Abbitt, Dr. Lee Biggerstaff, Dr. Alison Jones-Farmer and Dr. Fadel Megahed
Phi Delta Theta Project
In fall 2017, this team took on the task of deciphering if the current database system used by Phi Delta Theta's national headquarters met their business needs. From there, the team worked with the fraternity to build specialized reports generated by the database as well as trained the employees to use the more advanced features of the software.
Student Team: and
Faculty Mentor: Dr. Doug Havelka
Insurance Social Media Project
This team worked to gather tweets about an insurance company by interacting with the Twitter API. Then, the team categorized the tweets by using an algorithm. They were tasked with trying to better understand the public perception of the brand, how customer complaints were handled and responded to by their employees, and in general how often they were being tweeted about. Fall 2017.
Student Team andFaculty Mentor: Dr. Fadel Megahed
Economics World Bank Visualization Project
This team designed an interactive, exploratory visual of the World Bank data for the Economics Department in the fall.
Student Team: , and
Faculty Mentor: Dr. Fadel Megahed
2015-16 Project Archive
Butler County Land Bank Project
This project saw CADS continue work on the Butler County Land Bank project from the previous semester. The Butler County Land Bank is a nonprofit organization that takes title of abandoned, blighted residential properties for demolition and reutilization. In Fall 2016 this project team worked with data from local government offices to discover what impact the Land Bank had on the local community. The previous year, the team was able to show that the Land Bank has a positive economic effect on local housing. In the fall, work was continued to discover if there is a relationship between local crime rates, 911 calls, and the removal of blighted properties.
Student Team: , , , , , and
Facility Mentor: Dr. Allison Jones-Farmer, Dr. Mark Morris, Dr. Melissa Thomasson and Dr. Maria Weese
Healthcare Claims Project
This team consulted for one of the largest rehabilitation hospital systems in the country in fall 2016. They conducted a text analysis working with both structured and semi-structured data in R. At the conclusion of the project, the classification method was able to successfully categorize over 90% of the records that were given. The client was delighted at the algorithm and code that helped his team categorize and understand why Healthcare claims were being rejected.
Student Team:
Faculty Mentor: