Official course description, subject to change:
This course introduces fundamental and advanced concepts in statistics and probability from a data-science perspective. The aim of the course is for the student to be familiarised with probabilistic and statistical methods that are widely used in data analysis.
The aim of the course is to enable the student to work systematically with data sets with several variables which is important in regard to performing statistical analyses in data science. The course builds on the knowledge acquired in courses such as “Applied statistics” and “Machine Learning” and intends to give the student additional tools to identify, and solve statistical problems.
- The prerequisites required for admission to the course are Linear Algebra and Optimisation or equivalent (vectors and matrices, eigendecomposition, univariate calculus) and Applied Statistics or equivalent (basic probability theory, expectation and variance, univariate distributions, data presentation and visualisation).
- Students must be able to programme. The default language is Python, but other languages are possible.
Intended learning outcomes
After the course, the student should be able to:
- Analyze statistical problems and reason about the most appropriate methods to apply
- Apply and reflect on advanced applied statistical methods
- Identify and describe problems that can be solved using multivariate techniques
- Implement basic statistical algorithms and interpret results
- Summarize the results of an analysis in a statistical report
Ordinary examExam type:
D: Submission of written work with following oral, External (7-point scale)
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.