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.
The learners should not worry if they do not know any of these concepts beforehand – all methods and approaches will be introduced and explained in detail in video lectures and in-person practice seminars. This approach allows the learners to rewatch the topics that they feel less confident about and ask additional questions during the seminars. By the end of this course, the learners will have more knowledge of different data mining techniques and should be able to employ these for data-driven decision-making in their area of interest.