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.

The objective of the course is to provide students the knowledge and skills on data-driven decision making and change management in the context of e-governance.

This course is designed to introduce knowledge management and strategic planning concepts into an organizational change context and to provide an in-depth focus on data-driven decision-making in various leadership functions, including strategic planning and resource allocations in various organizations and institutions. The course examines models, tools, techniques, and theory of data-driven decision- making that can improve the quality of leadership decisions through case studies. Learning activities in the course will examine how decisions and strategies are developed and how tacit or explicit knowledge can be identified, captured, structured, valued and shared for effective use. Assessment as a dynamic process in the planning cycle will be examined. Organisational leaders are tasked with making difficult decisions based solely upon incomplete, inaccurate, misleading, and subjective information. As such, this course provides an introduction to theoretical and practical applications of data-driven decision-making and planning for change agents in organisations.

Upon successful completion of the course the participant:
– knows the basic issues and specificity of data-driven decision making and change management in organisations
– is able to co-design and adopt data-driven change management project;
– is familiar to use modern data analytics and visualization tools;
– understands organizational change;
– is able to evaluate effectiveness of various change management strategies and tactics on the organizational level;
– is able to use creative, innovative solutions in data visualization and interpretation;
– has an overview on the recent research and best practice on data-driven decision making in organisations;
– is able to express, present and demonstrate innovative ideas on change management in organisations.