Projects

students and advisers sitting in a circle discussing project

CADS hosts experiential learning projects for our students to refine and further develop their skills. The projects are given by companies including ones that have hired CADS, pro bono cases, and projects from our partners, which are completed free of charge. These projects allow students to apply what they have learned in educational settings to real world scenarios and to work with a multidisciplinary team to solve a problem.

Economics World Bank Visualization Project-Part 2

In the spring of 2018, the team continued the work from the previous semester and by extending the filtering capabilities of the visualization and compiled a database with data sources such as FRED and World Bank, that automatically provide up-to-date data.

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Call Forecasting Center

In the spring of 2018, a forecasting model was developed to better predict the client’s call center volume from week to week in order to optimize their staffing levels and their marketing budget.

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Ecological Data Paper

Nicholas Jerdack worked with Miami’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

Kai Li worked with Miami’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).

2017 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. 

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Cristo Rey Project

In the spring of 2017, the team was tasked to solve a transportation problem for Cristo Rey High School. 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.

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Phi Delta Theta Project

In the fall of 2017, the 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. 

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Insurance Social Media Project

In the fall of 2017, the team first gathered 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.

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Economics World Bank Visualization Project

In the fall of 2017, the team designed an interactive, exploratory visual of the World Bank data for the Economics Department.

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2016 Project Archive

Butler County Land Bank Project

In fall 2016, CADS continued 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. 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.

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Healthcare Claims Project

In fall 2016, the team consulted for one of the largest rehabilitation hospital systems in the country. 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.

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