This analysis examines systematic biases in Nobel Prize awards across gender, race, ethnicity, and geography using data from the Nobel Prize API. Our investigation reveals: Gender Bias: Only a small percentage of science Nobel laureates are women, with statistical evidence suggesting bias contributes to this gap. Racial Bias: There are no Black scientists who have won science Nobel Prizes in the historical record examined here. The Rosalind Franklin Case: A prominent example of uncredited contributions in the discovery of DNA’s double helix.

Submitting my homework 1!

A short presentation demonstrating simple linear regression in R using the mtcars dataset.

This analysis uses the Nobel Prize v2.1 API to explore patterns in Nobel awards across categories, countries, and decades. After flattening and cleaning the nested JSON data, I generated summary tables and visualizations showing which fields receive the most prizes, where laureates are born, and how awards have changed over time. The project highlights the challenges of working with nested API data and the importance of careful data inspection and step-by-step processing.

Analizar los comparendos de transito año 2010

Olivia Ogutu

Dow Jones Case Study DA 6813 - Data Applications, Fall 2025 UTSA, Dr. Roy Linear model of Dow Jones data, CAPM comparison with S&P 500 historical data for Jan - Jun 2011