Common Gaps in Identifying as Data Science Doers Among Undergraduate Students

The study investigates the common gaps that undergraduate students need to bridge to become and identify as doers of data science. Data science education plays a crucial role in shaping students' professional identities and influencing how they view themselves as active members and contributors to a professional community. The study participants included 39 undergraduate students enrolled in data science courses at a university in the Southeastern United States. These data science courses are open to students from all majors and programs on campus and do not require any prerequisites. Data was collected from a semi-structured interview prompt that asked students: "Can you think of one big change or smaller changes that you would need to experience to feel more comfortable identifying as a person who does data science?" Through thematic analysis, a total of seven themes were identified.

The findings indicated that gaining professional experience and a job title in data science represents the greatest gap identified by students. Students also identified earning a data science credential as an important gap in identifying as a doer of data science. Other gaps include: taking advanced data science coursework, developing related technical skills, applying data science in authentic contexts, working with complex data, and engaging in data science communities.

The outcome of this study presents ways data science programs can provide students with learning experiences that support the development of self-perception as data science doers. In particular, these findings could inform instructors and programs on designing and implementing authentic learning opportunities, such as working with real-world datasets and engaging in project-based learning, to equip students with applicable skills and professional expertise.

Data Science Education

Data Science Learners 

Education

Symposium on Data Science and Statistics (SDSS) 2025