Big Data Analysis

Database design methodology for NoSQL systems. The approach is based on NoAM (NoSQL Abstract Model), a new abstract data model for NoSQL databases that takes advantage of the common features of various NoSQL systems and is used to define a system-independent application. Overall, the methodology aims to support the scalability, performance, and consistency required for next generation web applications

The course is intended for a more advanced study of the Java programming language, including the study of JSP (Java Server Pages), Servlet, JDBC (Java Database connection), including many basic principles of Java to Enterprise Edition (Advanced Java EE).

The course based on concrete examples. Develop mathematical methods through examples and construct algorithms to solve concrete problems. The course includes the following topics: recursions, sums, integer functions, elementary number theory, binomial coefficients, special numbers, generating functions, discrete probability, asymptotics.

This course is an intermediate class covering the design of computer algorithms and the analysis of sophisticated algorithms. Students learn how to analyze the asymptotic performance of algorithms, and gain familiarity with major algorithms and data structures. They also apply important algorithmic design paradigms and methods of analysis, in addition to synthesizing efficient algorithms in common engineering design situations. Course materials are designed to help students understand the difference between tractable and intractable problems and to become familiar with strategies to deal with intractability.

This course is designed to learn the basics of mobile development. Mobile applications received as a result of the course can be downloaded to the university repositories and also displayed on the Play Store.

The discipline introduces students to the main directions in the development and use of data storage systems. The purpose of teaching the discipline: to create a base for the application of modern methods of data collection and analysis to solve practical problems and to develop students”” ability to create the necessary data warehouse architecture for analyzing large data sets in order to obtain aggregated information.

“Applied Machine Learning” is a discipline that focuses on the practical application of machine learning techniques to solve real-world problems and make predictions or decisions based on data. It covers a wide range of topics such as data preprocessing, feature selection and engineering, model selection and evaluation, supervised and unsupervised learning algorithms, ensemble methods, deep learning, and ethical considerations in machine learning. By studying this discipline, learners will gain a solid understanding of the key concepts and algorithms in machine learning and develop the skills to apply them effectively to various domains and datasets. They will learn how to preprocess and transform data, select appropriate features, train and evaluate models, optimize hyperparameters, interpret model results, and deploy machine learning solutions. The learning outcomes include the ability to identify suitable machine learning techniques for specific problems, build accurate and robust predictive models, and leverage machine learning to derive insights and make informed decisions from complex datasets.

The course is designed to study the basics of working with big data and the principles of high-performance computing. Big data implies the existence of huge arrays of structured and unstructured information, and the choice of tools for their efficient processing and extraction of useful information.

The course is aimed at studying the principles of operation of modern microprocessor technology, which is the basis of universal and specialized computers, and embedded systems, methods of organizing the interaction of a microprocessor with memory and external devices. In the course of studying the course, students should get an idea of the peculiarities of the internal structure of a modern microprocessor.

In this course student learns to implement agents based on Deep Reinforcement Learning, a type of machine learning where an agent learns to behaves in the environment by performing an action and acquiring responce. Students create agents using Tensorflow and Pytorch to learn on their own in simple games. By exploring this methods students will enter agents based on deep reinforcement learning in applied areas

Information Retrieval & Data Mining” is a discipline that explores the principles, techniques, and algorithms for efficiently extracting and analyzing useful information from large datasets. It encompasses various aspects such as information retrieval, which focuses on searching and retrieving relevant information from unstructured data sources like documents or the web, and data mining, which involves discovering patterns, relationships, and insights from structured and unstructured data. This discipline covers topics such as data preprocessing, retrieval models, indexing, query languages, data clustering, classification, association analysis, and evaluation metrics. By studying this discipline, learners will develop a solid understanding of the theoretical foundations and practical techniques used in information retrieval and data mining. They will acquire the skills to design and implement effective retrieval and mining systems, evaluate their performance, and apply them to real-world problems, enabling them to make data-driven decisions and derive valuable insights from vast amounts of information.

Study of natural language processing methods for the analysis of medical texts, including methods of classification, information extraction, automatic text processing and machine learning. During the training, students get acquainted with the principles of work and applications of natural language processing methods in medicine, as well as software tools for processing medical texts. After passing the discipline, students will be able to apply natural language processing methods to analyze medical texts, extract information and evaluate the quality of medical records.

It is a final project where students form teams and work on real problems in the field of electronic engineering. During the project, they apply the acquired knowledge and skills, developing the concept, designing, modeling, testing and implementing their solutions. They also develop communication and management skills by presenting their results in the form of presentations and reports. The Capstone project allows students to gain hands-on experience and prepare for a career in electronic engineering.

The course is designed to study the basic methods and tools required for the introduction of scientific research. The course also introduces students to the most popular search and scientometric databases of scientific articles, such as Web of Science, Scopus, ScienceDirect and others. During the course, students will become familiar with the tools for citing and searching for the required scientific information.

In the first part of the discipline “Statistics & Data Science” studentswill delve into the fundamental concepts and techniques of statistics. This section focuses on providing a strong statistical foundation that is essential for working with big data. They will explore topics such as descriptive statistics, probability theory, hypothesis testing, and regression analysis and learn how to summarize and interpret large datasets using measures of central tendency, dispersion, and graphical representations.

The second part of the discipline “Statistics & Data Science” students focus on the practical application of data science techniques to extract valuable insights from large-scale datasets. This section covers various aspects of data science, including data preprocessing, data visualization, machine learning, and data mining. They will learn how clean, transform, and preprocess big data to address common issues, such as missing values, outliers, and inconsistencies, ensuring data quality for further analysis; explore different visualization techniques to effectively present and communicate insights derived from big data, enabling you to identify patterns, trends, and anomalies; get familiar with machine learning algorithms and techniques suitable for big data analytics, such as classification, regression, clustering, and dimensionality reduction, enabling automated pattern recognition and prediction; understand the principles and methods of data mining to discover meaningful patterns and relationships in large datasets, facilitating decision-making and uncovering hidden insights.

The course presents the collection and analysis of materials for writing a graduation project

The course is devoted to the study of information security technologies.

Topics include (but not limited to) bioinformatics databases, sequence and structure alignment and differential gene expression analysis. In addition, students will also learn how to compare results between different samples.

In this course the possibilities of a legal attack on various web resources will be considered. As part of this course, students will learn how to find vulnerabilities and exploit them. Security bypass methods, the TCP/IP network protocol, Windows internals, and the Python programming language will also be covered.

The course focuses on the practical application of the MapReduce distributed computing model. For the implementation of the algorithms, a freely distributed set of Hadoop utilities has been selected, which is used to implement search and contextual mechanisms of many highly loaded websites during mass-parallel data processing. Currently, Hadoop is considered one of the fundamental technologies when working with Big Data and is used in many industries: healthcare, telecommunications, trade, logistics, financial companies, as well as in public administration.

Fundamental principles of business analysis that apply to projects of all sizes in agile and conventional business environments. Best practices, tools and techniques available to help business analysts achieve project goals and deliver business value. Facilitation techniques and modeling approaches, business analysis planning and monitoring, and requirements elicitation documentation. Agile and Lean approaches to problem definition, strategic and tactical analysis, design thinking principles and solution evaluation techniques. Aligned with the industry standard A Guide to the Business Analysis Body of Knowledge® (BABOK® Guide), published by the International Institute of Business Analysis® (IIBA®).

This course is intended to develop software systems and applications with focus on cloud solutions where it is most effective. Students have the opportunity to work with a variety of cloud technology providers such as Amazon, Google, Microsoft. They will learn how to deploy cloud solutions for databases, data analytics, and machine learning. The course contains following topics: “Load Balancing”, “Scalability, Availability and Fault Tolerance”, “BigQuery”, “Machine Learning on Unstructured Datasets”, etc.

Discipline introduces students to the fundamental concepts, techniques, and applications of generative models in artificial intelligence and machine learning. The course covers a range of generative techniques, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and autoregressive models, to generate new data samples from learned distributions. Students will explore the practical applications of generative models in various domains such as image synthesis, natural language processing, and creative AI, while gaining hands-on experience in designing, training, and evaluating these models using popular machine learning frameworks.

The aim of the discipline is to study the fundamental techniques for developing HPC applications, the commonly used HPC platforms, the methods for measuring, assessing and analysing the performance of HPC applications, and the role of administration, workload and resource management in an HPC management software. The students will be introduced to the issues related to the use of HPC techniques in solving large scientific problems.

The course forms students’ understanding of the field of information security, its constituent components, main threats, protocols and protection tools. During the study, students will acquire basic information security skills and become familiar with professional tools and programs.

Bioinformatics is an inter-disciplinary subject that develops and implements novel methodologies and tools for analyzing and learning from biological data.
This course covers the fundamental domain knowledge needed from both biological and computational disciplines to enable further study and research in this subject with a strong practical and theoretical emphasis to increase understanding. No previous knowledge of Bioinformatics is required.

The course covers the area of risk management in a project context; provides basic theories and concepts of risk management applicable to project environments, including planning, preparation and response to project risks; and examines the areas of risk identification, assessment, monitoring and control. The course will introduce students to qualitative and quantitative risk analysis techniques.

The course is designed for students to complete a project, a finished minimum product, which they will be able to present at various competitions (hackathons). The course does not imply a lecture, and all classes will be practice-oriented, with the maximum emphasis on obtaining the results of the finished product. During the course, students should apply all the knowledge gained in the second year, including knowledge on the development of ready-made applications.