The course consists of discussing algorithms in the field of modelling, complex systems simulations and resulting experimental big data sets analysis. Further, methods for system modelling will also be discussed, and prime categories of tasks in the area of their continuous, discrete, and combined simulations will be defined. Then, students will be introduced to languages based on semiformal (UML) or formal approaches (Petri net, Pi-calculus). Planning and subsequent realization of simulation experiments lead to big data sets, which then need to be analysed using methods based on neuron meshes, nearest neighbour method in highly dimensional data, flow data processing, identification of association rules, clustering, algorithms of analysis and big graphs structure detection, techniques for obtaining important characteristics from big data sets using reduction of dimension and algorithms of machine learning such as perceptron meshes and SVM (Support Vector Machines). Within the course, an emphasis will be put on applying methods optimized for HPC servers and methods which are currently being developed for accelerators.