Understanding data before you model: statistics and analysis as the foundation of every classic AI application.
A model is only as good as the analysis beneath it. If you do not see through distributions, outliers and samples, you build models on quicksand — and draw conclusions that are wrong.
This module makes you critical: you learn to assess model outcomes, distinguish correlation from causation and recognise when figures appear to say more than they do.
You analyse data from your own organisation and learn to draw conclusions that stand up to scrutiny.
An HR analyst investigates whether absenteeism is really rising — or whether the composition of the workforce is changing.
A marketer explores which customer groups are leaving and formulates testable hypotheses about why.
A product owner assesses whether the measured difference between two variants is meaningful or coincidence.
You take this module the way you take the whole programme: classes every other week on Friday and Saturday, with a study load of 15–20 hours per week, of which 10–15 hours is self-study. The teaching is a mix of classroom sessions, workplace learning, blended learning and working groups or study teams — taught by lecturers who practise the profession themselves on a daily basis.
You conclude each theme with a professional product or a technical solution addressing a real situation in your own work, which you discuss in an assessment with the lecturer. This way your portfolio grows with real work — and your employer benefits directly.
After this module you can independently carry out a data analysis with conclusions you can back up statistically.
Email or call the programme team — we are happy to think it through with you.