Refresh old knowledge or learn something new in 2025!

In 2025, we have a total of 15 scheduled courses in R, including two brand new. The new courses are titled R 4 – Survival analysis and Biomarker data (Statistics in medicine) & R 5 – Machine learning and AI.

If you have any questions about this or anything else, feel free to contact us here.

R 4 – Survival analysis and Biomarker data (Statistics in medicine)

This course will cover methods for survival analysis including visualisation techniques such as Kaplan-Meier plots, and regression models such as Cox proportional hazards regression. During the second day you will learn how to best analyse biomarker data, which has become a vital part of modern medicine.

Survival analysis
Kaplan-Meier curves
Comparing groups
Regression models for survival data: Cox and AFT models
Competing risks, recurrent event and time-dependent variables in survival models

Biomarker data
Visualisation of biomarker data
Two-sample tests and regression for biomarker data with detection limits
Strategies for finding relevant biomarkers
Multivariate analysis of sets of biomarkers

Next course is on Zoom May 13–14, 2025

Learn more about R 4

A problem I have returned to many times over the years, both as a researcher and as a consultant, is the statistical analysis of biomarker data. Biomarkers are measurements from, for example, blood tests that describe the levels of various proteins or molecules. They are an important part of modern medicine and are widely used in medical research, where scientists search for biomarkers that can be used to diagnose diseases.

Biomarker data is characterized by several challenges: many variables but few observations, data that is not normally distributed, and measurements that fall below the detection limit of the measurement technique. This requires specialized statistical methods.

Måns Thulin

Some thoughts from the course developer on the statistical analysis of biomarker data

R 5 – Machine learning and AI

During this course we learn how to train different kinds of machine learning models and how to evaluate the predictive performance of them. We will also discuss how modern AI systems work and build models for analysing text and images.

Machine learning
Evaluation of predictive models: test-training-splits, cross-validation
Regularised regression (lasso)
Nearest neighbours models
Decision trees, random forests and boosted trees

Artificial intelligence
Deep neural networks
Using AI models for analysis of text data
Using AI models for analysis of images

Online courses on Zoom April 23–24 and June 3–4, 2025

Learn more about R 5

Course leader

Måns Thulin

Appreciated educator with extensive statistical expertise

Måns Thulin has developed all the R courses offered by Statistikakademin. He works as a consultant and lecturer in statistics, machine learning, and artificial intelligence. His clients include large corporations, government agencies, startups, and researchers. By using advanced statistical analysis, he has solved problems in a wide range of areas, from antibiotic resistance to nuclear fuel, from milking robots to HR issues, from herniated discs to music videos. He has twelve years of teaching experience at institutions such as Uppsala University and the University of Edinburgh. Måns is also the author of the popular textbook Modern Statistics with R. His educational goal is to help all course participants understand statistical methods – statistics should feel logical, not like black magic.

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