01
Biological question
Start by defining the biological question, organism, sample type, measurement platform and outcome of interest.
Bioinformatics pathway
Learn bioinformatics through biological data and interpretation.
This pathway helps learners connect biological questions with computational data structures, statistical reasoning and careful interpretation of omics and biomedical data.
Suggested route
02
Data type
Understand common omics and biological data types, such as sequence, expression, methylation, protein or spatial data.
03
Processing ideas
Build intuition for quality control, normalisation, alignment, feature extraction and data structure.
04
Analysis and statistics
Connect biological data analysis with statistical reasoning, multiple testing, modelling and uncertainty.
05
Biological interpretation
Interpret outputs carefully in biological context, considering limitations, reproducibility and evidence strength.
Recommended resources
Read these guides alongside the pathway.
Data analysis · Foundation
How to prepare your data before analysis
5 min read · Updated 5 June 2026
A detailed guide for students learning how to clean, check, structure and document data before running statistical analysis.
Statistics · Foundation
Understanding p-values, confidence intervals and effect sizes
6 min read · Updated 5 June 2026
A detailed guide explaining statistical significance, uncertainty, effect size, practical importance and how students should interpret results responsibly.
Research methods · Intermediate
Common mistakes in dissertation data analysis
5 min read · Updated 5 June 2026
An advanced guide to the most common statistical, methodological and reporting mistakes students make in dissertation data analysis, with practical ways to avoid them.
Regression · Intermediate
How to report regression results in a dissertation
5 min read · Updated 5 June 2026
An advanced guide to reporting linear, logistic and adjusted regression results clearly in dissertation chapters, including interpretation, tables, confidence intervals and limitations.
Data analysis · Intermediate
Missing data: deletion, imputation and reporting
6 min read · Updated 5 June 2026
An advanced guide to understanding missing data mechanisms, complete-case analysis, imputation, bias, sensitivity and transparent reporting.
Statistics · Advanced
Multiple testing and false discovery rate
5 min read · Updated 5 June 2026
An advanced guide explaining why repeated hypothesis testing increases false positives, how family-wise error and false discovery rate differ, and how to report multiple-testing corrections.
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