The following is a listing of demos of student projects done in the Spring 2025 Principles of Computing course. The students found their choice of data source from resources online and decided how they want to examine, analyze and visualize the data.
| Section | Project ID | Topic/Link | Members |
|---|---|---|---|
| 2 | 1 | Mental Health Trends | Ava Sorrentino, Magdalena Regenauer |
| 2 | 2 | Global Life Expectancy and its Indicators | Creed Leathers, Aryan Patel |
| 2 | 3 | Chicago public health and education | Hailey Butusov, Kathryn Fine |
| 2 | 4 | Mothor Vehicle Collision NYC | Pomaa Anim-Amofa, Lindsay Abad |
| 2 | 5 | Happiness and World Data | Dora Vrenezi, Peter Nguyen |
| 2 | 6 | Chicago Crime and School Analysis | Scott Monroe, Angel Benitez |
| 2 | 7 | Chicago Public Schools | Andrew Zian Wang, Patrick Boyd |
| 2 | 8 | Crime Data Across Three U.S. Cities | McKenzie Reitmayer, Marcela Rodriguez |
| 2 | 9 | Crime and Incarceration in the United States | William Mueth, Angel Salazar |
| 2 | 10 | Filipino-American Mortality | Francine Vudoti, Kristofer Ulanday |
We took world wide life expectancy data from Kaggle. The data set was extremely rich and didn't need to be combined with other datasets. We compared life expectancy across continents and different characteristics such as health care spending and GDP. We also created a breakdown of every continent, charting the continent countries' life expectancy and distributions. Inside the continent breakdowns, we also included country "spotlights" at the bottom of the page (two countries per continent).
The project does a crime analysis into three major U.S. cities: Chicago, New York, and Los Angeles. Crime data was found from each city's respective police department and investigates specific details about crimes committed such as crime locations and types of crime. Some aspects that the project visualizes includes top crime locations, top crime types, and maps of crime locations. The project includes visualizations specific to each city and then visualizations comparing data across the different cities. Some major insights from the project are that the top crimes and locations of crimes from each city are really based on the infrastructure of the city in terms of tourism and transportation available. Additionally, the data visualizes top crime locations to allow users to make more educated decisions about the places they go and choose to live within the city.
Presenting visualizations that explore patterns and potential correlations between crime rates and educational outcomes across Chicago's community areas. By examining data such as graduation rates alongside crime statistics, this dashboard aims to highlight trends that may inform community development, policy decisions, and public awareness. Users can investigate how educational achievement and public safety intersect geographically, providing insight into the broader social landscape of the city.
This project explores U.S. crime and incarceration trends using data from the Bureau of Justice Statistics and the FBI’s Uniform Crime Report. With charts, maps, and 3D plots, we visualized how crime types and prison populations have changed across states and over time. A key insight is that despite massive increases in incarceration, crimes like aggravated assault and larceny remain dominant.
The project was meant to cover factors of well being and happiness to analyze data across the globe to determine how those factors impacted each country whether economic, social, or even statistical data. The data came from Kaggle and covered the world happiness report, economic factors such as imports/exports/GDP, and various other factors. Aspects which we are visualizing include analyzing how economic and social factors impacted the happiness score as well as how those factors are interconnected and affect each other. Major insights drawn from the project include that typically happier countries border each other and their factors are interconnected. Moreover, there are certain insights on how various factors are not related to each other such as health affecting life expectancy, which is a major contradiction to popular belief, if the data was properly created and analyzed.
This project focus on the causes and effects of mental health on society. Multiple data sets were used in order to gain wide insights on the different aspects and different communities. The visualizations were grouped into three main categories: Work & Education, Lifestyle, and Age & Gender. Examples of data include Sleep Hours v. Screen Time v. Happiness Level, or Physical Activity Hours v. Stress Levels. This is helpful to people in the workforce, educators, or even the mass public, as mental health affects so many people worldwide.
Our topic is Chicago schools. We looked at demographic data and public school data to see if there were any demographic or education-based trends. We visualized test scores, safety scores, and neighborhood racial makeups mainly. Explore student performance, demographics, and school-level insights across Chicago's public education system.
We analyzed data form two datasets. The first included data about Chicago public health as well as demographics like unemployment, poverty, housing, and more. The second dataset had data about Chicago public education, which provided information about test scores and student performance as well as parent and family involvement, teachers, school safety, and student misconduct. Our project looked into how safety, parent involvement, poverty, and a few other factors vary in each region of Chicago and how those factors do or do not correlate to student performance in Chicago public schools.
Our topic pertains to the collisions in the 5 boroughs of New York City. These data shows the correlation between time of day, gender, vehicle type and the number of collisions. It also shows what kind of borough has the most number of collisions and analyses the number of collisions caused by certain circumstances.
This project focuses on analyzing and comparing the leading causes of death in the United States and the Philippines for the year 2021, aiming to uncover underlying health challenges and potential areas for public health improvement in both nations. Utilizing data from the World Health Organization's Global Health Estimates, the Centers for Disease Control and Prevention, and the Philippine Statistics Authority, we examined mortality rates, demographic structures, and health behaviors. We also had visualizations such as bar graphs, pie charts, scatter plots, choropleth maps, population pyramids, and line graphs to depict the top causes of death, contributing risk factors, geographic distributions, and demographic trends. Key insights reveal that ischemic heart disease and stroke are predominant causes of death in both countries, with lifestyle factors like smoking, obesity, and hypertension playing significant roles. The United States shows higher mortality rates from Alzheimer's disease and certain cancers, while the Philippines faces greater challenges with tuberculosis and lower respiratory infections. Geographical disparities in stroke mortality within the U.S. and a declining trend in smoking rates in the Philippines highlight the importance of targeted public health interventions. Overall, we hope through our efforts we may underscore the need for tailored health strategies to address both shared and unique challenges faced by the U.S. and the Philippines.