Analysis of business administration undergraduate courses with data mining methods: Recep Tayyip Erdoğan University case

Technology and the innovations brought by technology take their place in every part of life rapidly. It is the duty of the databases to carry the burden of these innovations. Evaluating data that is a meaningless burden and cost factor in databases is the area of interest of data mining. Data mining, which can be used in every field where data is stored, has been applied in the field of education in this thesis. The aim of the study is to analyze undergraduate courses. For this purpose, the course and student information of 731 students enrolled in Recep Tayyip Erdoğan University, Faculty of Economics and Administrative Sciences, Department of Business Administration since 2009 were examined. Grade-based relationships between the courses are examined with the Association Rules. According to the course grades, graduation scores, Student Selection Exam scores, Student Selection Exam placement rankings, passing the courses at first intake, with Classification algorithms were examined. Apriori, Naive Bayes, Sequential Minimal Optimization and J48 algorithms are the data mining algorithms used in the analysis.