An integrated, modular approach to data science education in microbiology

Area of Science:

• Bioinformatics
• Computational Biology
• Life Sciences Education

Background:

• Biological research is increasingly data-intensive, shifting focus to data interpretation.
• Current undergraduate bioinformatics and data science training for life scientists is insufficient.
• There's a need for accessible data science curricula to prepare students for modern biological research and careers.

Purpose of the Study:

• To describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative.
• To outline a framework for progressively building data science competency in undergraduate life science students.
• To address barriers in data science education for life scientists.

Main Methods:

• Integrating data science modules into existing required and elective life science courses.
• Augmenting coursework with coordinated co-curricular activities.
• Leveraging a community of practice including TAs, postdocs, instructors, and faculty across disciplines.

Main Results:

• Preliminary surveys show improved student interest and experience in bioinformatics and computer science after module completion.
• The EDUCE initiative addresses barriers such as instructor capacity and student prior knowledge.
• The framework is designed to be flexible and extensible for broad adoption.

Conclusions:

• The EDUCE initiative offers a viable model for integrating data science into undergraduate life science education.
• This approach enhances student preparedness for data-intensive biological research.
• The program aims to improve research and learning outcomes and career readiness for life science students.