This course is designed to give an overview of applying data mining in an educational context to students without a computer science or programming background. Some examples of the data mining methods that will be learnt include supervised machine learning algorithms like Decision Trees, Rule Induction, Support Vector Machines, and Artificial Neural Networks. In addition to that, unsupervised learning methods like clustering and association rule mining will be practised.

Even though most of the examples are in the context of education, the data mining methods taught in this course can be applied to any other context (e.g., e-commerce). This course equips participants with capabilities to implement bottom-up methods to find existing patterns and relationships in their datasets and employ predictive analytics to predict future results. The learners will experience the hands-on practice of implementing various data mining techniques on different synthetic and real-world datasets in free open-source software.

Accommodation

Tallinn Summer School arranges accommodation for foreign participants at student dormitory (5-minutes walk from the university, double and triple rooms)