In the work, an analysis of modern methods of Educational Data Mining (EDM) was carried out, on the basis of which a set of methods of EDM was developed for the training of vocational education teachers. The basic methods of EDM are considered, namely Prediction, Clustering, Relationship Mining, Distillation of Data for Human Judgment, Discovery with Models. The possibilities of using artificial neural networks, in particular, networks of Long-Short-Term Memory (LSTM), to predict the results of the educational process are described. The main methods of clustering and segmentation of educational data are considered. The basic methods of EDM are complemented by specialized methods of digital image pre-processing and methods of artificial intelligence, taking into account the peculiarities of the training of future specialists in engineering and pedagogical specialties. As specialized methods of digital image pre-processing, methods of filtering, contrast enhancement and contour selection are used. As specialized methods of artificial intelligence, methods of image segmentation, object detection on images, object detection using fuzzy logic were used. Methods of object detection on images using convolutional neural networks and using the Viola-Jones method are described. To process data with a certain degree of uncertainty, it is proposed to apply the methods of EDM and Fuzzy Logic in a integral manner. Ways of integrating Fuzzy Logic with methods of data clustering, image segmentation and object detection on images are considered. The possibilities of applying the developed complex of specialized methods of EDM in the educational process, in particular, when performing STEM (Science, Technology, Engineering and Mathematics) projects, are described.