Exploration of best practices for data visualization across biomedical and life science disciplines. Through hands-on exercises and real-world case studies, students will develop skills for displaying research data in compelling visual formats. Emphasis will be placed on translating data into compelling narratives that resonate with diverse audiences, with a particular focus on developing informative figures for peer-reviewed manuscripts and scientific presentations.
Athena Title
Data Visualization - Life Sci
Semester Course Offered
Offered spring
Grading System
S/U (Satisfactory/Unsatisfactory)
Student learning Outcomes
Students will be able to discuss the fundamental principles of data visualization and their importance in communicating complex biological concepts effectively.
Students will be able to examine various types of biological data, including molecular, ecological, and genomic data, and differentiate suitable visualization techniques in each category.
Students will be able to implement best practices for data cleaning, preprocessing, and data wrangling to prepare biological data for visualization.
Students will be able to compare and contrast the most common data visualizations used to highlight specific biological trends and patterns.
Students will be able to select and critique techniques for effectively incorporating color, labels, and annotations to enhance the interpretability of visualizations while avoiding common pitfalls.
Students will be able to describe and use the principles of data storytelling and how to create compelling narratives using different visualizations.
Students will be able to appraise ethical considerations and best practices in data visualization, including accurately representing data, avoiding biases, and ensuring data privacy.
Topical Outline
Why is data visualization so important?
Bad visualizations, and what they can teach us about best practices
Principles of effective data storytelling
Color theory and its application in life sciences visualizations
Overview of common visualization tools and software
Overview of common types of charts, plots, and graphs
Design principles for creating clear and informative charts and graphs
Tips and tricks for labels, annotations, and figure organization
Ethical considerations in data communication and visualization
AI and machine learning-driven visualization
Case studies on specialized visualization tools and techniques in the life sciences
