Miles Patton, 2026, of Amite, shares the findings from the final project with peers and teachers at LSMSA.
As Louisiana prepares to implement a statewide computer science graduation requirement, students at the Louisiana School for Math, Science, and the Arts in Natchitoches are already applying coding skills in innovative and unexpected ways, a news release said.
LSMSA students recently presented final projects from Data Analysis for the Humanities, a new interdisciplinary course that blends computer science with history, literature, music and cultural studies.
The course is cross-listed for both computer science and American history credit, allowing students to meet future academic requirements while exploring nontraditional applications of data analysis.
Years ago, the state required only a semester of computer science before eliminating the requirement entirely. However, beginning in 2031, Louisiana students will be required to complete a full year of computer science coursework.
Anticipating that shift, LSMSA faculty members Brad Burkman (computer science, Ph.D.) and Casey Green (history, Ph.D.) collaborated to design a course that aligns with upcoming standards while appealing to students with strong interests in the humanities and arts.
“Digital Humanities is becoming an increasingly important sub-field since it allows broader access to emerging research in history, English literature, and related disciplines,” Green said. “Data Analysis for the Humanities not only is an innovative approach to new curricular standards; it aligns LSMSA’s curriculum with that of major universities across the country.”
Rather than focusing solely on numerical data sets, the course treats cultural artifacts such as texts, music, and historical records as data. Students learned to use Python programming and Jupyter notebooks to collect, clean, analyze, and visualize large data sets over a six-to-seven-week research period.
“In a typical data analysis course, students learn to use spreadsheets and at least one programming language to pull in data, do analysis, and generate a report with visualization,” Burkman said. “In our Data Analysis for the Humanities course, the data could also be text, and the visualizations were often maps.”
Burkman explained that students learned to prepare data by removing capitalization, punctuation, and artifacts such as page numbers before analysis. They then worked with data sets containing hundreds of thousands of rows, applied machine learning tools, and generated both traditional graphs and geographic visualizations. Final projects were presented through slideshows and interactive websites.
One student analyzed professional basketball statistics, comparing players from the 1980s and 1990s, including Michael Jordan and Scottie Pippen, with modern athletes such as LeBron James, Stephen Curry, Nikola Jokić and Dwyane Wade. The analysis examined player efficiency, performance trends, and the impact of teammates across different eras.
Another student explored acquisition data from The Metropolitan Museum of Art, using year of acquisition and country of origin to examine geographic representation within the museum’s collection. The findings were presented through Python-generated maps.
Music also served as a data set, with one student conducting sentiment analysis on the complete discography of R.E.M. to study how lyrical tone shifted as the band transitioned from an independent label to a major record company.
Another student examined the relationship between physical demands and compensation in professional sports by analyzing factors such as strength, speed, durability, metabolism and injury rates. The research revealed patterns showing that athletes in team sports generally earn more than those in individual sports.