Postgraduate Programme in Practical Epi-Statistics

Turn data into decisions that matter.

 

Learn R in a real-world epidemiology context and develop practical skills to analyse, interpret and communicate health evidence.

A programme designed for epidemiological analysts at all levels, requiring only basic knowledge of data analysis.

 

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WHAT MAKES THIS PROGRAMME DIFFERENT

 

Most courses teach statistics.
This one teaches you to think epidemiologically using real-world data.

In the Postgraduate Programme in Practical Epi-Statistics, you will work directly with data inspired by one of the most influential studies in the history of epidemiology – the Framingham Heart Study – and learn how to use R to answer real health-related questions.

No unnecessary abstractions.
No theory disconnected from practice.

A programme that brings together epidemiology, statistics, R programming and artificial intelligence in a modern and highly relevant learning approach.

 

WHAT WILL YOU GAIN

 

By the end of the programme, you will be able to:

• Design epidemiological studies;
• Analyse data in R, from basic to advanced levels;
• Interpret results rigorously;
• Work with models such as logistic, linear, Poisson and Cox regression;
• Estimate measures such as relative risk, incidence rate ratio, odds ratio and hazard ratio.

 

Most importantly, you will know what to do with the results.

 

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WHAT WILL YOU LEARN

 

Practical learning from day one.

• Face-to-face sessions with close academic support;
• Continuous work using RStudio;
• Real-world discussion of results;
• Direct support from lecturers throughout the classes.

 

This is not a programme where you learn to click through menus.
It is a programme where you learn how to think, model and make decisions.

 

WHO IS IT FOR

 

This programme is designed for professionals who work with, or wish to work with, health data:

• Physicians and healthcare professionals;
• Researchers;
• Epidemiologists;
• Clinical trial analysts;
• Professionals working in the pharmaceutical industry and CROs;
• Statisticians and health economists.

 

WHY R

 

Because it is free, powerful and increasingly essential.

Unlike many other tools, R offers:

• Flexibility in data analysis;
• Advanced and innovative modelling capabilities;
• Integration with artificial intelligence;
• Scientific reproducibility that is easy to adapt and scale.

 

And, above all, independence.

 

Epidemiology is changing.

The way we analyse data is changing too.

The question is: will you keep up with that change?

 

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