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

Angela K Hilliker1, Kristine L Grayson1

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This study introduces a new undergraduate biology course focused on data exploration and visualization skills, equipping students to handle complex biological datasets without prior coding knowledge.

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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.