Recent years have seen an exponential growth in the volume and types of data available, and analytics professionals are needed to process and understand these data so that organizations can improve operations, marketing, profitability, and service. Hence, the Data Analytics field is essential in modern organizations.

The success of information systems over the last decades promoted huge databases. These databases have the potential to foster the development and significantly increase the wealth of our society, allowing problems’ resolution. Generally, it is acknowledged that these data repositories can provide precious resources on improving the quality of decision making. Data Mining is concerned with the conversion of data into knowledge for decision making and as such constitutes the central phase of the process of extracting knowledge from databases. It may be defined as the use of machine learning and statistical analysis to uncover patterns, relationships and other valuable information from large data sets.

Data Mining is, therefore, a central subject in Information Systems and Data Science related curricula. Its primary goal is to allow students to gather skills in extracting information and knowledge from large databases; it teaches students how to use the techniques and tools involved in data analysis and apply them to real world problems.

Bachelor 3rd year students, from the Information Systems for Management study programme, at Portucalense University have a Decision Support Systems subject in their study plan, dedicated to the topic of Data/Web Mining. These students were offered an applied data mining course, in the form of 2 workshops, combining theory with hands-on application, where the concepts taught were applied to real world problems. The course was lectured by one of the authors, as part of an Erasmus+ teaching staff program, held under a bilateral agreement between The Marie Curie-Sklodowska University, Poland and Portucalense University, Portugal.

This paper describes the experiences obtained within the data mining course, supervised by the authors, held as an Erasmus+ Teaching Staff Programme, aiming to apply data mining techniques to find patterns (clusters) under the topic “Analysing the attractiveness of European countries regarding tourism for senior citizens”.

The practical, real world, case addressed consisted of analyzing the attractiveness of European countries for seniors’ tourism. Datasets from the Eurostat database were used, namely, number of years of healthy life expectancy of citizens, risk of poverty rate for people over 65, purchasing power adjusted to GDP per capita, median income ratio relative number of people over 65 years old, overnight stays by tourist age group.

The students performed the ETL (extraction, transformation and loading) process of this data and used data mining techniques to create country clusters, taking into account the variables mentioned and the degree of attractiveness for senior tourism obtained.

Hence, using the clustering data mining technique, applied in datasets extracted from the Eurostat database, groups of countries were found among which tourism potential of people aged 65+ is at a similar level. The Tableau software was used for data analysis and visualization.

The work of the students will be overall described, the teaching-learning methodology and outcomes assessed, and lessons learnt will be highlighted.

Keywords: Data Mining, Clustering, Teaching staff mobility.