Non-Degree / Dates: 18-29 July 2022

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.

Why this course?

  • You will understand the basics of data mining methods and learn how to implement them in a free open-source software.

  • Access video lecturers and experience hands-on practice.

  • You will learn how to use bottom-up methods to find existing patterns and relationships in datasets, helping you to improve your decision-making skills.

Teacher(s)

Danial Hooshyar, an associate professor of educational data mining in the School of Digital Technologies at Tallinn University.

Timetable

Classes take place each week from Monday to Friday. The lectures are planned for each day starting at 10:00. Please see also the more detailed programme.

Participants

Anyone interested in understanding the basics of data mining.
Anyone interested in mining data using machine learning algorithms.
Anyone interested in learning RapidMiner.

Limited to 20 participants

Credit points

Upon full participation and completion of course work students will be awarded 2 ECTS points and a diploma of completion.

Course fee

Early-Bird Course Fee (until 31 March 2022)400€
Regular Course Fee (after 31 March 2022)500€

Accommodationcultural programme and meals are not included in the the price.