Data Mining
Beginning with an introduction to ensemble methods, the course moves into sophisticated classification techniques alongside approaches for evaluating these models.
Students gain hands-on experience with advanced techniques for dimensionality reduction and clustering and their respective evaluation. The curriculum also covers time series analysis and forecasting methods, equipping students with skills for temporal data challenges.
In addition, the course includes Explainable AI (XAI) and survival analysis, providing a well-rounded view of practical and theoretical data science tools.
By the end, students are prepared to understand, apply and critically assess advanced data science models, preparing them to navigate complex real-world datasets effectively.
Teaching Format
The teaching consists of lectures and guided exercises.
The teaching takes place in English.
During the course, a number of programming assignments must be solved. Those assignments must be approved before Written exam 1 can be taken. Written exam 1 checks that the student has understood the assignments that have been done.
Assessment
The course is examined through written exams:
Written exam 1, 3.5 credits, grade G/U
Written exam 2, 4 credits, grades A-F
Examiner
Study counsellors
Visiting hoursPlease contact us via email if you want to book a meeting. We are available on Campus in Kista and via Zoom.
Phone hoursThursday 12.30–2 pm
Irregular office hoursFirst phone hours for spring 2026: Thursday 15 January
