|Introduction to Cloud Computing||AIN1-042||5||Introduction to cloud computing. Cloud computing platforms. Parallel programming in the cloud. Distributed storage systems. Virtualization. Security for cloud computing. Multi-core operating systems.||To provide students with theoretical knowledge and practical skills in cloud computing.||DLO 1 - know cloud computing platforms, advantages and disadvantages of different cloud computing platforms.
DLO 2 - use skills to analyse the trade-offs between deploying applications in the cloud and on-premises infrastructure, deploying applications in commercial cloud computing infrastructures such as Amazon Web Services, Windows Azure and Google AppEngine.
DLO 3 - program data-intensive parallel applications in the cloud.|
|Privacy decision support||AIN1-043||5||Decision support system. Structure of the decision support system . Features of a decision support system. Decision support applications. Comparative decision support systems for approvals. Comparative decision support systems for confidentiality||To provide students with theoretical knowledge and practical skills in the use of various decision support tools for privacy||DLO 1 - to know the behavioral and normative theories of decision making, the value of decision support systems for individuals and organizations.
DLO 2 - use skills to design decision support systems and processes.
DLO 3 - to acquire new knowledge in the area of privacy decision support|
|Introduction to Big Data||AIN1-044||5||The concept of Big Data. Features of collecting, storing, processing and analysing big data. Sources of big data. Use of big data in science, business, public administration. Options for building distributed databases, replication, fragmentation. Consistency. CAP-theorem. Classes of NoSQL databases. Examples of NoSQL database. Families of columns. Graph DBMS. Data consolidation tasks. Multidimensional data storages. Relational data storages. Virtual storages. Fuzzy environments. Introduction to ETL. Data transformation in ETL. Enrichment of data. Map- Reduce technology. GOOGLE BIGTABLE. Full-text search. Parallel queries. Principles of data analysis. Structured data. Preparing data for analysis. KDD and Data Mining Introduction to data transformation. Transforming ordered data. Clustering of the data. Data fusion. Quantization. Normalizing and coding the data. Principles of data analysis. Structured data. Preparing the data for analysis. KDD and Data Mining techniques. Introduction to data visualization. General purpose visualizers. OLAP analysis. Visualizers for model quality assessment. Visualisers for model interpretation. Identifying patterns in decision trees, logical rules, neural networks. An overview of modern software tools for data analysis: Statistica, SPSS, Excel, R-Studio, KERAS. The means of building distributed information systems for BigData. Methods of data analysis.||The formation of students' professional competence in the development and use of big data.||DLO 1 - to know decision-making methods based on big data.
DLO 2 - to use skills in applying data analysis methods to decision making.
DLO 3 - to work in a team, independently acquire and use new knowledge and skills of big data input|
|Basics of Scientific Research||AIN1-045||5||Creativity in science and design work. An overview of methods of technical creativity. Methods of scientific research in engineering. Classification of research methods. Information and patent search. Setting up an experiment. Systematization of information. Planning research methods. Mathematical processing of experimental results. Drawing up R&D results. Registration of the research report.||To develop theoretical knowledge of the current state, directions of development and implementation of scientific research in the field of profile direction.||DLO 1 - to know the basic logical methods and techniques of scientific research, methodological theories and principles of modern science, the basis of modern computer technology, criteria of dependence of features and homogeneity of data, the criteria of significance of parameters.
DLO 2 - use skills in logical and methodological analysis of scientific research and its results, application of mathematical methods in technical applications, patent search, planning of scientific experiment, public speaking, argumentation, debate and argumentation.
DLO 3 - carry out methodological justification of scientific research, assess the effectiveness of scientific activity, use network technologies and multimedia in education and science; select criterion parameters depending on the requirements for product quality and production costs, formulate the research task based on production needs, identify distribution functions, justify criterion parameters.|
|Number Theory||AIN1-046||5||Studying methods of research and solving of integer equations, properties of prime and composite numbers, laws of distribution of prime numbers in natural number series and arithmetic progressions||To learn methods of research and solution of integral equations, properties of prime and composite numbers, laws of distribution of prime numbers in a natural number and arithmetic progressions||DLO 1 - to know properties of prime and composite numbers, laws of distribution of prime numbers in natural number series, properties of rings of deduction classes on natural modules, basic properties of algebraic extensions of field of rational numbers and finite fields, properties of arithmetic functions
DLO 2 - use skills in solving linear and quadratic equations of several variables, systems of linear equations in integers, as well as establishing solvability and finding solutions of algebraic comparisons and systems of comparisons, exponentiation comparisons, finding systems of first-order roots, constructing rational approximations to real numbers.
DLO 3 - to acquire and use new knowledge and skills on number theories independently.|
|Machine Learning||AIN1-047||5||Motivation. Data manipulation. Visual analytics. Clustering. Regression. Classification. Deep learning.||To form students' knowledge of conceptual foundations of machine learning and practical skills in working with tools, models and methods of machine learning.||DLO 1 - to know the key concepts, goals and objectives of using machine learning; methodological foundations of applying machine learning algorithms.
DLO 2 - use skills to visualize the results of machine learning algorithms, and interpret the results.
DLO 3 - work in a team, independently acquire and use new knowledge and skills in machine learning.|
|Privacy and Ethics||AIN1-048||5||Ethics and information technology. Completeness of information. Plagiarism in an online environment. Identity in cyberspace. Avatars and anonymity. Privacy. Synthesis. The ethics of gaming. Cheating in games. Virtual environment and ethics.||To provide students with theoretical knowledge in the field of ethical and privacy issues related to new information technologies.||DLO 1 - to know current models of information ethics, ethical standards regarding new technologies, legal and ethical aspects related to security and privacy of information technologies.
DLO 2 - use skills on ethical standards to interpret personal and group behavior when using different information technology tools.
DLO 3 - to acquire new knowledge using modern educational and information technology in the field of ethical issues and privacy.|
|Robotics||AIN1-049||5||An introduction to the concept of robotics. Microcontrollers and microprocessors. Sensors and types of sensors. Actuators. Basics of electronics. Microcontroller programming. Studying Arduino IDE. Studying Tinkercad. Working with LEDs, creating traffic lights. Working with potentiometer, controlling the brightness of LED. Working with a photoresistor, creating automatic lighting. Working with pushbuttons. Operation with a servo-motor, creating a manipulator. Operation with a gear motor, creating a machine with remote control.||Formation of students' theoretical knowledge and practical skills in robotics.||DLO 1 - to know theoretical basics on sensors, types of signals (discrete/analog), actuators.
DLO 2 - use skills in selecting equipment for the required task, programming microcontrollers and creating circuits for the task.
DLO 3 - work in a team, independently acquire and use new knowledge and skills in robotics.|
|C# Programming||AIN1-051||5||The concept of class. Basic elements of a class. Overloading of operations in a class. Inheritance in C#. Object-oriented features of C#. Working with files. Generalized classes (templates). Working with graphical objects.||To form students' theoretical knowledge and practical skills in C# language.||DLO 1 - to know the features of programming languages, algorithms, libraries and software packages development, system and application software products in C# language.
DLO 2 - to use skills to develop architecture, algorithmic and software solutions for system and application software.
DLO 3 - work in a team, independently investigate the development and use of tools, automated systems in scientific and practical activities.|
|Basics of Economics||AIN1-052||5||Introduction to economics. Subject matter and method. Basic economic concepts. The market system: supply and demand. Costs and profit of a firm. Financial and monetary system. Inflation and unemployment. Open economy.||Formation of students' systematic economic thinking to understand the economic logic of the laws of development of society, processes and phenomena occurring at the micro level, mastering modern methods of microeconomic analysis with the possibility of further application of the acquired knowledge in practical activities.||DLO 1 - to know the general conceptual apparatus of economic theory, basics of the theory of public choice, economic efficiency and political decision-making.
DLO 2 - use skills to identify problems of economic character in the analysis of specific situations, propose ways to solve them, use knowledge of economics and economic policy to solve professional problems.
DLO 3 - to acquire new knowledge using modern educational and information technologies and to work independently with economic literature|