• Course code:63546D
  • Credits:6
  • Semester: summer
  • Contents

Data analysts, people who are able to, with a combination of computer and statistical/mathematical knowledge, extract useful information from data, are today one of the most desired and sought-after employees. The main objective of this course is to equip the student for solving most data analysis tasks we encounter in every-day statistical practice and empirical research, while at the same time introducing the theoretical background and algorithms that make statistical analysis possible.

We will first learn the fundamentals of statistics, in particular, Bayesian statistics. Bayesian statistics is a modern approach to solving statistical problems and is in many ways more intuitive than classical statistics. It is also well suited to those who have a background in computer programming and algorithmic thinking. We will get familiar with the concepts of statistical modelling, prior and posterior probability, and classical and modern algorithms for statistical inference (Gibbs, Metropolis-Hastings, Hamiltonian Monte Carlo). In the second half of the course emphasis will be on applying what we have learned to solving practical problems, from simple ones up to current research problems. From data preprocessing. through modelling, to presenting results. We will look at the tasks of prediction, clustering, hypothesis testing in various different fields, such as predicting the outcome of sports matches, modelling airflow trajectories and air pollution, clustering brain nodes with similar activity patterns, and many more... We will be using the R programming language and the modern Bayesian inference tool Stan.

Most of the final grade will be from graded lab-work and homework and a smaller part from a written exam. Students that perform well in the first half will be offered the opportunity to work, with guidance, on a real-world applied statistics problem instead of the written exam.





  • Study programmes
  • Distribution of hours per semester
45
hours
lectures
30
hours
laboratory work
  • Professor