Machine Learning Practitioner
Build the mathematics, programming and modelling skills required to understand and apply machine learning effectively.
Build machine learning from the mathematics upwards.
The aim is not simply to complete the stages below. Each stage should add a capability that makes the next one easier and moves you closer to independent application.
Develop this capability progressively as you move through the roadmap.
Develop this capability progressively as you move through the roadmap.
Develop this capability progressively as you move through the roadmap.
Develop this capability progressively as you move through the roadmap.
Develop this capability progressively as you move through the roadmap.
Develop this capability progressively as you move through the roadmap.
7 stages from foundation to application.
Follow the stages in sequence unless you already have strong evidence that you have mastered an earlier requirement.
Mathematics for Data Science
Build the foundation required for the rest of the pathway. Spend enough time here that mathematics for data science feels usable rather than merely familiar.
Statistics Foundations
Develop your capability in statistics foundations and connect it with the knowledge from earlier stages before progressing further.
Python for Data Science
Develop your capability in python for data science and connect it with the knowledge from earlier stages before progressing further.
Machine Learning
Develop your capability in machine learning and connect it with the knowledge from earlier stages before progressing further.
Advanced Machine Learning
Develop your capability in advanced machine learning and connect it with the knowledge from earlier stages before progressing further.
Model interpretation
Develop your capability in model interpretation and connect it with the knowledge from earlier stages before progressing further.
Machine learning project
Bring the earlier stages together through machine learning project. The aim is to demonstrate independent application rather than isolated technical knowledge.
Progress by mastery, not by calendar.
The suggested duration is a guide. Move faster through skills you already have and spend longer where your foundations are weaker.
Diagnose
Identify your starting pointReview the early stages and decide which foundations you genuinely already understand.
Learn
Build concepts carefullyUse structured courses, explanations and practice to build the skills required at each stage.
Test
Check independent understandingMove on when you can explain and apply the ideas without following a worked example step by step.
Apply
Build something realComplete analyses, projects, research workflows or technical tasks that combine multiple stages.
This pathway may cross several disciplines.
Modern technical work rarely fits inside one academic subject. Use the wider platform to strengthen any supporting areas you discover along the way.
Statistics
Probability, inference, modelling and real-world decision making.
Mathematics
From algebra and calculus to proof, linear algebra and optimisation.
Data Science
Python, R, SQL, visualisation, machine learning and real projects.
Bioinformatics
Genomics, transcriptomics, single-cell, spatial and computational biology.
Computer Science
Programming, algorithms, software, databases, systems and artificial intelligence.
Hit a difficult stage?
Get expert help without abandoning the path.
Use tutoring for difficult concepts, technical troubleshooting, project feedback, research methods or guidance on what to learn next.
Compare related pathways.
Your goal may change as you learn more about the field. Explore other routes before deciding how deeply you want to specialise.
Data Scientist
Build a complete foundation in mathematics, statistics, Python, SQL, visualisation and machine learning.
Data Analyst
Develop practical skills in spreadsheets, SQL, statistics, visualisation and Python for real-world data analysis.
Biostatistician
Progress from statistical inference and regression to epidemiology, survival analysis and reproducible health-data analysis.