Code from: Beyond the classroom: Alicia’s multivariate journey

The importance of data science skills for modern scientific research
cannot be understated. Although policy documents increasingly
recommend what skills should be included in undergraduate
statistics and data science curricula, little is known about how
students actually develop and apply these skills. This
paper addresses this gap through an in-depth case study tracing
one student’s learning progressions throughout her master’s
program. Using a qualitative method to analyze student code, which has
seen little use in statistics education research, I examined how
Alicia transferred the data science skills from her applied
statistics course into authentic research settings. The analysis
shows that, while Alicia successfully navigated new challenges,
she encountered persistent hurdles when extending bivariate
techniques into multivariate contexts, particularly with
visualizations and summary statistics. These findings
highlight the obstacles students may face when applying classroom
knowledge to real-world data problems. The results carry
implications for instructors designing curricula, researchers
studying how students learn data science, and policymakers
shaping educational standards, underscoring the need to pair
policy recommendations with research on the realities of student learning.