Nowadays we live in a world full of data, that surrounds us everywhere. Data helps us to test hypotheses, recognise patterns, and make discoveries, whether it is primitive or complex; heterogeneous or homogeneous (Larose & Larose, 2014). Research and data are intertwined. They are interconnected with the most important stages of academic pursuit, i.e., collection and analysis of information. The data analysis is designed to recognise the patterns and achieve clarity in the phenomenon under study.
Data plays a specific role in any science. In education, for instance, it helps to enhance the quality of teaching (Bienkowski et al., 2012). Personalisation of education becomes more accessible. Qualitative and quantitative metrics of a student’s academic progress and preferences allow teachers to choose the content, tempo, and methods of teaching, depending on the student’s individual needs. Moreover, those metrics provide the means for the academic progress prognosis, and, when in university, academic attrition. The content, namely, the educational programme can be improved with the help of the gathered data. A collection of learning assessment materials can be updated to ensure the quality of education. The data is also significant in the management of an educational institution. The administration is able to track the finances, plan the budget, and effectively coordinate the whole system on the basis of the data. Likewise, it is interconnected with innovations in education, helping in the development of new online courses, e-textbooks, and apps focused on ensuring availability and learning efficiency.
Working with data implies its visual representation. There are many guidelines and papers on the graphical representation of the data (Maaten & Hinton, 2008; Glazer, 2011; Yau, 2024). The most significant aspects will be covered hereinafter.