The discipline focuses on Python language programming. During the course, students learn the Python syntax, the features of the SciPy, NumPy, matplotLib, Pandas libraries, and some built-in libraries for solving power industry applied problems. The discipline forms an idea of how to present a mathematical problem as a program code. Students learn to represent and visualize data and experimental results.

The discipline addresses data representation in various machine learning problems. The discipline provides an overview of the fundamental data types: numerical tabular data, time series, images, textual data. Lectures cover the theoretical aspects of data analysis, including probability theory and mathematical statistics. As part of the laboratory sessions, case studies of various methods and approaches application are considered. In addition, the course provides an overview of the main tools for working with big data and cloud computing.

The discipline studies the main machine learning techniques and their application in the power industry. The following sections and machine learning techniques are considered: supervised learning in classification and regression problems (linear models, support vector machines, decision trees, Bayesian methods, k-nearest neighbors, neural networks, ensemble algorithms), unsupervised learning (clustering algorithms), reinforcement learning and optimization methods (genetic and swarm algorithms), fuzzy logic. Special focus is given to preliminary analysis and data processing approaches.

The discipline studies the basic principles of assuring data security and providing protection against cyber threats for power system facilities. The discipline covers legal and organizational issues, software specifics, technical and algorithmic methods of data protection used to assess the threats and to model the corresponding protection measures. Special consideration is given to the existing legal framework in the sphere of data protection and the relevant industrial standards. The discipline forms deep understanding of the types of data to be protected, the classification of cyber threats at power system facilities, various protection techniques, the principles of their operation and selection criteria. The project is implemented by the winner of the Master's Program Faculty Grant Competition of the Vladimir Potanin Fellowship Program.

The discipline studies the principles, methods and tools of studying complex objects by representing them as integrated systems. The purpose of studying the discipline is to consider the theoretical foundations and patterns of systems’ design and operation, including methodological principles of their analysis and synthesis. The studied approaches will be applied for critical analysis, interpretation and generalization of research results in the professional area and making strategic decisions.

The discipline is focused on IEC 61850 standard and other related technologies ensuring the performance of the digital power industry facilities. In particular, the course studies a general concept of the substation information model, a description of a specialized markup language for creating a substation model according to IEC 61850 standard. Case studies of digital substations considered in detail allow to gain skills in equipment setting up to implement a communication traffic between the digital substation’s units.