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The course offers an in-depth study of probability and statistics that is relevant for further studies in data science. The aim of the course is for the student to develop a deeper understanding of stochastic processes and the ability to apply statistical inference in handling large datasets.

The course contains the following components:

- Probability theory: functions of random variables, central moments, generating functions and convergences of random variables
- Bayesian statistics: Bayes theorem, posterior distribution, maximum-likelihood method (MLE), maximum-A posterior estimation (MAP)
- One-factor experiment design, data collection and data quality
- In-depth regression analysis: weighted least squares method, autoregression, non-linear and logistic regression

Advanced use of statistical software

Syllabus - here you can also find course literature

Earlier syllabuses

Malmö University provides students who participate in, or who have completed a course, with the opportunity to express their opinions and describe their experiences of the course by completing a course evaluation administered by the University. The University will compile and summarise the results of course evaluations. The University will also inform participants of the results and any decisions relating to measures taken in response to the course evaluations. The results will be made available to the students (HF 1:14).