Teaching biology students data exploration and visualization in a data-driven world.

Area of Science:

• Biological Sciences
• Data Science in Biology

Background:

• Increasingly large and complex datasets in biology necessitate enhanced data exploration and visualization skills among students.
• Traditional biology curricula often lack sufficient time to develop these crucial data handling competencies.

Purpose of the Study:

• To develop and implement an upper-level undergraduate biology course focused on data exploration and communication.
• To provide students with essential data management and visualization skills without requiring prior coding experience.

Main Methods:

• The course emphasized data visualization principles and best practices.
• Students learned to manage and visualize data using Tableau and R.
• The curriculum incorporated modules on scientific ethics, misinformation refutation, and data-related inequities.

Main Results:

• Students gained practical skills in data exploration and visualization applicable to complex biological datasets.
• The course successfully integrated data handling techniques with critical thinking about scientific integrity and equity.
• Participants developed proficiency in using Tableau and R for biological data analysis.

Conclusions:

• The developed course effectively addresses the need for data literacy in undergraduate biology education.
• This approach empowers students with transferable skills in data management, visualization, and ethical data handling.
• The curriculum provides a model for other institutions to enhance their biology programs with data science components.