Onze opleidingen

Originally SPSS was an acronym that stood for 'Statistical Package for the Social Sciences'. In the meantime, the software package has evolved into SPSS Statistics and one of the most used statistical packages in the world.

R is a flexible environment for statistical computing and graphics, which is becoming increasingly popular as a tool to get insight in often complex data.

In a rapidly changing (professional) context, lifelong learning has become a must. With microcredentials Ghent University offers a new type of course for a broad group of lifelong learners.

This course aims to provide insight into basic statistical concepts with emphasis on practical applications. Mathematical formulae will be kept to a minimum. The theory and the methods of analysis will be extensively illustrated with examples relating to a wide variety of different fields.

Tidyverse is a collection of R-packages used for data wrangling and visualization that share a common design philosophy. The goal of this course is to get you up to speed with the most up-to-date and essential tidyverse tools for data exploration.

Python started off as a general-purpose programming language, but in the last decade it has become a popular environment for data science.

To emphasize the practical approach in this course all classes will take place in a pc room.

Modern high throughput technologies easily generate data on thousands of variables; e.g. health care data, genomics, chemometrics, environmental monitoring, web logs, movie ratings, … 

Linear regression addresses how a continuous dependent variable is associated by one or more predictors of any type. The fact that many practical problems deal with continuous outcomes (e.g.

NVivo is a widely used computer assisted qualitative data analysis software package which provides a potentially useful tool for the management and analysis of qualitative research data. This course is intended as a basic introduction to using NVivo for qualitative data analysis.

The scientific method is historically linked to the possibility that other researchers can replicate and verify its results. As scientific analysis becomes more complex and interdisciplinary, ensuring reproducibility becomes more challenging specially in fields that combine different expertise.

In the age of Artificial Intelligence, many modern digital applications increasingly rely on machine learning as a means to derive predictive strength from high-dimensional data sets.

This course will give a broad overview of topics in meta-analysis.