Data Visualization in Biological Sciences
About
Biology runs on data—and knowing how to visualize it sets you apart.
Build data visualization skills across genomics, proteomics, and microbiomes. Today’s life-science careers require the ability to turn complex data into clear, meaningful insights.
Gain relevant and practical skills
Designed with input from BC life-sciences employers, this program focuses on practical, job-ready skills you can use right away.
Courses emphasize hands-on work with realistic datasets, collaborative workflows, and reproducible practices that carry over directly to research labs, biotech startups, and health-tech environments.
You’ll work with real-world biological datasets from areas such as drug discovery and biomarker research, learning how to create visuals that researchers, clinicians, and industry teams understand and trust.
Who this program is for
Early-career biologists, bioinformaticians, and data analysts who want stronger visualization and communication skills for biological data
Graduate students, research assistants, and lab staff who regularly work with large datasets and need to make results clear to collaborators and stakeholders
Professionals and career-changers from computing, data, or health-related fields who want to enter or advance in BC’s life sciences and biotechnology sectors
Recommended experience
The program uses script-driven workflows for data preparation and visualization. You should be comfortable with basic coding concepts. Prior experience with Python or a similar language is recommended.
What you’ll learn
By the end of this program you will be able to:
Frame biological questions as testable workflows and prepare datasets to research and production standards
Build reproducible pipelines using scripts, version control, and runbooks that support peer review, grant reporting, and regulatory-quality work
Deploy and scale containerized workloads in the cloud, optimizing cost, performance, and reliability for sustained analyses
Select, validate, and sanity-check analytical and visualization methods; define baselines and metrics; and clearly state limitations and reproducibility
Communicate insights through effective figures, dashboards, and concise written briefs tailored to investigators, clinicians, and product stakeholders
Collaborate in modern data teams, packaging notebooks, containers, and datasets as shareable, re-runnable artifacts
Apply privacy, security, and Indigenous data sovereignty principles, documenting consent and provenance and using culturally safe practices
Translate findings into next steps such as experiments, prototypes, or deployment plans, articulating risks and resource needs
Program structure
Program runs in the evenings and on weekends from March 20 - April 20, 2026
The micro-credential includes four, 1-credit courses.
Each credit takes 12 hours to complete.
Courses are typically delivered in a mixed-mode format (online plus scheduled components).
Select the 'Courses' tab for more detailed information.
