DSC Archive

The goal of the project was to explore public perception on COVID-19 vaccine by analyzing social media platform data (Twitter).

The project aim was: 1) Understand how the degree of mental health issues changed over time and space during COVID-19; 2) Find out what topics are people concerned about, and 3) Infer what group of people are more likely to have mental health issues.

To spur museum membership growth, encourage donations from members, and increase overall museum revenue

The Caldwell-Fay equation (2002) attempts to model what Lake Ontario's current water level would be if dam construction had never taken place along the St. Lawrence Seaway (i.e. the natural hydraulic state of the lake). Newly unearthed Lake Ontario data going back to the 1860s has been discovered, and we had the rare opportunity to be the first to digitize and publicly analyze it. Since this data set predates any dam construction it actually captures the lake's natural state. Therefore it can be used to verify Caldwell-Fey's equation which is being used to govern the lake's inflow and outflow rate on a daily basis.

We were given a patient reported symptoms dataset PRO-CTCAE and applied a variety of clustering methods. The clusters were then statistically tested for associations with a selection of outcomes such as hospitalization. We found significant associations with clusters and outcomes and compared it to linear regression results.

RTS is a regional transportation authority established by New York State and the goal of the project is to find the potential reasons for preventable accidents caused by bus operators. First, descriptive and exploratory analysis is performed on all the data provided and driver-related variables and environmental-related variables. Then, frequent pattern mining is applied and conditional probabilities are calculated for the accident history of operators with high risk of accidents to extract accident patterns.

Wegmans grocery stores experience changes in consumer demand due to weather-related events which may result in item shortages. Our goal was to generate a list of items that are expected to have a huge increase in sales which would allow Wegmans to prepare beforehand. We correlated the change in consumer demand over time with weather warning data and detected anomalous behaviors in item sales.

In this project, we want to apply DSC and machine learning techniques to identify and analyze group communication and interaction patterns from the data collected, e.g. “Who interacts with whom” and “Who attended which breakout sessions”, which can function as an indicator of team performance, group intelligence and meeting efficiency. We can further use the information to increase the productivity of Un-meetings by modifying related elements.