School of Bioinformatics · Programming & data
Scientific Data Visualisation for Biologists
A figure is often the only part of a paper that readers study closely. This course teaches the principles and tools for figures that are accurate, accessible and ready for a journal.
Course overview
Scientific Data Visualisation teaches figure design as a scientific skill: choosing the chart that matches the question, showing uncertainty and individual data points, using colour that survives colour-vision deficiency and greyscale printing, and avoiding visual distortions.
You build figures in ggplot2 and matplotlib, assemble multi-panel figures to journal specifications, and create interactive plots for exploration and supplementary material.
Learning outcomes
By the end of the course you will be able to:
- Choose chart types that match the scientific question
- Show distributions, uncertainty and raw data honestly
- Apply accessible colour palettes and typography
- Build reproducible figures in ggplot2 and matplotlib
- Assemble multi-panel figures that meet journal requirements
Skills you will practise
Eligibility and prerequisites
Who can apply
- Students and graduates of B.Sc., M.Sc., B.Tech., M.Tech., B.Pharm., M.Pharm., MBBS, BDS, B.V.Sc. or a PhD programme in the life sciences, biotechnology, bioinformatics, pharmacy, medicine, agriculture, chemistry, statistics or computer science
- Researchers, faculty and industry professionals moving into data-driven biology
- Final-year students may apply before their results are declared
Prerequisites
- None; basic R or Python helps
Best suited to
- Students and researchers preparing theses and papers
- Anyone who makes figures in Excel or GraphPad and wants more control
- Bioinformaticians who present results to biologists
Module-wise syllabus
5 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Perception and visual encoding
- Chart choice
- Common distortions
Topics
- Box, violin and raw-data plots
- Error bars and confidence intervals
- Small multiples
Topics
- Sequential, diverging and categorical palettes
- Colour-vision accessibility
- Typography and labelling
Topics
- ggplot2 grammar
- matplotlib and seaborn
- Themes and reuse
Topics
- Multi-panel assembly and export
- Journal size and resolution rules
- Interactive plots with Plotly
Practical sessions and projects
Capstone project
Redesign your figures
Take a set of figures from your own work or a public paper and rebuild them reproducibly to a journal standard, with a short note on each design decision.
Assessed on the correctness of the analysis, the quality of the code and the reasoning in the write-up. A pass is required for the certificate.
- Principles
Critique published figures and redesign two of them.
- Distributions and uncertainty
Replace a bar chart of means with a figure that shows the data and its uncertainty.
- Colour and type
Recolour a heatmap and a multi-group plot for accessibility and greyscale.
- Figures in code
Rebuild a publication figure in both R and Python from the raw data.
- Journal and interactive figures
Assemble a four-panel journal figure and an interactive companion version.
Software, tools and datasets
Software and tools
All software is free and open source or free for academic use. Recommended computer: 8 GB RAM, 30 GB free disk, Linux, macOS or Windows with WSL2.
Datasets
- Palmer penguins and airway RNA-seq tables: Plotting exercises.Open R packages
- Figures from open-access papers: Critique and redesign.PLOS and eLife (CC-BY)
Core reading
- Rougier NP, Droettboom M, Bourne PE (2014). Ten simple rules for better figures. PLOS Computational Biology 10:e1003833.
- Wilke CO. Fundamentals of Data Visualization. O’Reilly, 2019.
Duration, format and fees
- Next cohort
- Dates are sent with your admission offer
- Timetable
- Three evening or weekend sessions a week (IST), plus a lab clinic; intensive and weekend-only schedules on request
- Payment
- Secure online payment link with your admission offer (UPI, cards, net banking). Instalments on request for fees above ₹15,000.
- Refunds
- See the refund and cancellation policy.
Instructor
The instructor for each cohort is named in your admission offer and on this page when the cohort is announced. Vedicgen instructors are selected for hands-on research or industry experience in the subject they teach, and every course is reviewed by a second subject expert.
Questions about who teaches a cohort? [email protected]
Certificate
Learners who complete the course and pass its capstone receive a Vedicgen certificate of completion for Scientific Data Visualisation for Biologists, issued by Vivofold, stating the guided hours and skills assessed. Each certificate has a unique ID and QR code that anyone can check.
Apply for Scientific Data Visualisation for Biologists
Apply
Send the form below. It takes about five minutes.
Review
An adviser confirms your fit and format within two working days.
Offer
You receive dates, timetable and a secure payment link.
Onboard
Set-up guide, pre-reading and joining links.
Frequently asked questions
In the live cohort, modules are taught in instructor-led sessions with a weekly lab clinic where you work on real public data with help at hand; every session is recorded. The self-paced format uses the same lessons, labs and datasets with mentor review of your capstone. Both formats end with an assessed project reviewed for the code and the science.
A laptop with at least 8 GB of RAM and about 30 GB of free disk space, running Linux, macOS, or Windows with WSL2. Installation instructions are sent before the first session, and lab clinics help with any set-up problems.
Yes. Learners who complete the course and pass its assessment receive a Vedicgen certificate of completion, issued by Vivofold. Every certificate carries a unique ID and a QR code that anyone, including an employer or admissions office, can check on the certificate verification page.
Apply with the form on this page. Live and in-person cohorts run through the year; the dates, timetable and joining details of the next cohort are sent with your admission offer and listed on this page once announced. Self-paced enrolment opens as soon as your application is accepted.
Yes. No course runs longer than four weeks, and the length is customisable: choose the standard schedule, an intensive one- to two-week schedule, weekends only, or a custom timetable. Pick your preference on the application form; colleges and teams can book their own dates.
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