Applied Data Analytics

This discipline involves the study of the most current programming technologies, such as the Python programming language and the R statistical data processing language. The course will also cover the basics of managing relational and non-relational databases.

This course explores innovative pedagogical approaches and effective techniques for engaging and facilitating learning in diverse educational settings.

These disciplines involve the study of the basics of statistics, linear algebra, mathematical analysis and discrete mathematics required to form the mathematical basis of data Analytics.

This discipline involves the study of the basics of machine learning and artificial intelligence, and the application of this knowledge to solve real-world applications. The discipline covers many topics of teaching with and without a teacher. A third type of machine learning task, called attachment learning, is partially covered.

Research practice

A case study on data Analytics is designed to give students the opportunity to apply first-year competence to a real-world project, preferably with professional training. A case study is basically an analytical and descriptive task involving the selection and analysis of a suitable business process or production process in the workplace. This process is described, modeled, and improvement goals are defined. Students decide where and what data to collect in the process chain. They also generate the corresponding data set.

The purpose of the discipline “Big Data Methods and Tools” is to provide master students with an in-depth understanding of big data concepts, technologies, and methodologies, enabling them to process, analyze, and derive insights from large-scale datasets in various domains

This discipline involves learning the basics of understanding how to work with data, and extracting the required information from the data. The discipline “making decisions based on data” involves the study of the business component, i.e. how to apply data analysis to make the right management decisions.

This course introduces students to various generative modeling techniques and algorithms, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and autoregressive models. Students will learn how to design, train, and evaluate generative models, as well as explore their applications in fields like computer vision, natural language processing, and data synthesis

The course covers the area of the service approach in organizing the company’s activities; service tools and services provided by internal divisions and/or external contractors

This discipline involves learning the basics of business process analysis and design using the most popular design methodologies, such as BPMN 2.0, EPC, and others.

The course is designed to study the management of stakeholders (stakeholders) of the project. Undergraduates will consider the basic principles and analysis of the external and internal environment of the project, aimed at identifying and systematizing the main stakeholders, assessing their goals, collecting information about them and using this data in the project management process. It will also consider negotiating and engaging stakeholders to collaborate with managing the expectations of key stakeholders.

Discipline that focuses on the strategic planning, development, and successful execution of products throughout their lifecycle. It includes a wide range of activities and responsibilities aimed at creating valuable and marketable products that meet customer needs and meet business goals.

This course is a concept, languages, methods and patterns for programming heterogeneous, massive parallel processors. It covers heterogeneous computing architectures, software programming models, memory launching methods and parallel algorithms on the example of CUDA and OpenCl.

This course will introduce students to the basics of reinforcement learning. Upon completion of this course, the student will be able to: Formalize problems as Markov decision-making processes; Understand basic exploration techniques and trade-offs between exploration and exploitation; Understand value functions as a universal tool for making optimal decisions; Know how to implement dynamic programming as an effective approach to solving industrial control problems.