Data visualization for life science, 1.5 hp

Course numberNN
TrackCancer, Cardiovascular diseases, Drug development, Infection, Inflammation, Metabolism, Muscoloskeletal system, Neuroscience, Reproduction and development,
Max participants32
Application deadline2022-11-15
Course leaderAlenka Guček
DepartmentDepartment of Medical Cell Biology
Visiting addressHusargatan 3, Uppsala
Post addressBox 571, 751 23 UPPSALA
Datesweeks 48, 49
Course length1 full week
Course report 
Course plan 



Due to enormous technical advancement in life science field in the last couple of years new ways how to visualize the large quantity of data in a meaningful and clear way are being developed. Not only bioinformatics, but also life science researchers will need to be equipped with tools and competences to communicate their work to both peers through effective scientific visuals and to society in general.
The course is aimed primarily at PhD students with at least some experience in making scientific figures but is also open to postdocs and researchers who would like to understand and learn more about data visualization in scientific research.
The aim of this course is to educate next generations of life science researchers how to visualize the research data in a better way and develop a healthy level of criticism and data literacy.

Learning outcomes

Intended learning outcomes for students are to acquire skills and tools to visualize their data in a way that their research is understandable to others; to develop data literacy and critical thinking while studying published literature; and to demonstrate reflection on best practices in life science dataviz.


Course is a combination of lectures, workshops, individual and group work. Lectures will cover the core concepts of the data visualization, chart types, perception and data abstraction, graphical components and use of colors in data visualization, best data visualization practices from bioinformatics, how not to mislead with charts, and multivariate data visualization. Workshops will cover the practical instructions on how to preprocess and visualize the data with open source tools such as Python and R (no programming background is required) and allow students to develop a critical eye by bringing real-life examples of scientific figures and discussing what works and what doesn’t. For the individual assignment, students will develop a visualization for the COVID-19 Data & Code Reuse Competition ran by the For the group assignment, students will put the acquired knowledge to practice by preparing visualizations for different types of audience (publication, presentation or SciComm). Students can work on their own data or we provide it.


Lectures and workshops will be held online. Students will receive the instructions for the individual and the group work assignment when the course begins.


Student knowledge will be assessed by their assignments: short presentation of visualizations for both individual and group work, the active feedback that they will provide to fellow students and active participation on the workshops.


Fundamentals of Data Visualization, Claus O. Wilke []

Teaching staff

Alenka Guček, MCB, UU and AI-lab, JSI
Anders Has, IT, UU
Liane Hughes, data visualization at Scilifelab
Cedric Scherer, data visualization designer, Berlin
Benjamin Cooley, data visualization engineer, Broad Institute, MIT
and other invited speakers

More information

Alenka Gucek
Department of Medical Cell Biology

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