Beginner to professional · 26 stages
A practical path from sequence data to research readiness.
Every stage explains what to learn, why it matters, how to practise and what evidence to produce. Follow the sequence, revisit skills as projects demand, and mark a stage complete only when you can demonstrate it independently.
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The learning sequence
Learn the idea. Practise it. Show your work.
Stages are grouped into five phases. Open any stage for clear explanations, guided practice, tools and a portfolio outcome.
- PHASE 01Programming foundationsStages 1–8
- PHASE 02Core bioinformatics & statisticsStages 9–15
- PHASE 03Genomics specialismsStages 16–21
- PHASE 04AI & advanced researchStages 22–24
- PHASE 05Research & career readinessStages 25–26
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From learning to evidence
Five portfolio projects.
Each project builds on the stages before it. Project 1 is published; the RNA-seq pipeline is in development.
Illustrative part-time schedule
A 24-month outline, not a deadline.
Move at a pace that supports durable practice. Apply for suitable roles or programmes when you meet their requirements — completing every stage is not a prerequisite for every opportunity.
- Months 1–4Python, algorithms, Linux, Git, scientific Python, file formats and statistics.
- Months 5–9R, Bioconductor, RNA-seq, workflow management, containers and HPC foundations.
- Months 10–16Cancer genomics, databases, variants, single-cell, epigenomics, proteomics and multi-omics.
- Months 17–20Machine learning, deep learning, a focused specialisation and research practice.
- Months 21–24Consolidate the portfolio, prepare targeted applications and practise communicating methods.
Human genomic data can carry controlled-access conditions. Check dataset licences and privacy requirements before downloading or sharing data. Learning projects are not clinical evidence or diagnostic tools.