Computer Scientist

Semester: Autumn
ECTS credits: 4
Lecture hours: 20
Practical class hours: 20
Assessment: Credit (pass/fail)
Language of instruction: Ukrainian

Lecturer: Yurii Ivashyna

“Theory of Mechanisms” is one of the fundamental engineering courses, providing the basic knowledge and skills needed to understand, analyse, synthesize and design the mechanisms that underlie many machines and technical devices. It is essential for training specialists in mechanical engineering, instrument making, robotics, automotive engineering, mechatronics and related fields. The course covers a wide range of topics related to the principles of mechanism design, laws of motion, kinematic and dynamic properties, methods of analysis and synthesis, and the basics of computer modelling of mechanical systems. The aim of the course is to give students a system of theoretical knowledge and practical skills needed for: analysing existing mechanisms; understanding the kinematic and dynamic processes in mechanical systems; independently designing new mechanisms; and preparing for related courses such as “Machine Parts” and “Theory of Machines and Mechanisms”.

Objectives of the course: students should master the basic concepts and terms of the theory of mechanisms; learn to classify and analyse mechanisms by type of kinematic pairs and structure; understand the operating principles of planar and spatial mechanisms; master methods of kinematic analysis and synthesis; be able to calculate velocities, accelerations and forces in mechanical systems; and use software to model mechanisms.

Semester: Autumn
ECTS credits: 4
Lecture hours: 20
Practical class hours: 20
Assessment: Credit (pass/fail)
Language of instruction: Ukrainian

Lecturer: Maksym Poltoratskyi

The course “Functional and Logic Programming” introduces students to modern approaches to software development, in particular functional programming in Scala and object-oriented programming in Java. The aim of the course is to give students a basic understanding of the principles of functional programming and practical skills in developing complex, high-performance applications. Students study the key features of Scala, such as pure functions, recursion, immutability and working with collections, as well as integrating functional and object-oriented styles. Working with Java, students deepen their understanding of building scalable applications, architectural patterns and code optimization methods. The course combines theory with practice: students develop their own applications, work on real programming tasks and compare approaches in Scala and Java, which helps them better understand the strengths of each language. This also lays a solid foundation for further work in back-end development, high-load systems or big data. The course develops analytical thinking, the ability to write clean and maintainable code, and the ability to adapt to different programming paradigms.

Semester: Autumn
ECTS credits: 4
Lecture hours: 20
Practical class hours: 20
Assessment: Credit (pass/fail)
Language of instruction: Ukrainian

Lecturer: Vitalii Kobets

Master the art of working with data to make successful investment decisions!

Do you dream of becoming a successful investor? Do you want to learn to analyse data so that your investment decisions outperform the market? The course “Software in Business Analytics” will open up a world of opportunities at the intersection of technology and finance!

Under the guidance of Doctor of Economics, Professor Vitalii Kobets, you will gain practical skills in using RStudio to analyse data, build forecasts and create investment strategies. The course focuses on applying machine learning — using both programming languages and no-code/low-code tools — in investment processes, which makes it valuable for students of all IT specialties.

Why choose this course?

• You will gain practical skills in using R to analyse and interpret data and build forecasts in the subject area
• You will learn to apply linear and logistic regression, classification and other machine learning methods to support investment decisions
• You will be able to build and interpret decision trees and neural networks for business analytics
• You will gain a competitive edge on the job market with data-driven analytical skills that employers value highly

Course features:

• A practical approach using RStudio and real datasets
• Study of modern analytics and forecasting methods on real data
• Development of your own algorithms for analysing investment opportunities
• Mastery of visualization tools that turn complex data into clear conclusions for decision-making

Learn to speak the language of data and make sound investment decisions with the course “Software in Business Analytics”! Unlock the potential of analytics, machine learning and artificial intelligence to build a successful career in a world where data drives the digital transformation of business.

Sign up now and invest in your own future!

Semester: Autumn
ECTS credits: 4
Lecture hours: 20
Practical class hours: 20
Assessment: Credit (pass/fail)
Language of instruction: Ukrainian

Lecturer: Hennadii Kravtsov

Course aim: to develop a system of knowledge and skills in the methodology and technology of developing a distance course, enable future specialists to engage in high-quality professional and pedagogical communication, and support students’ self-realization and self-improvement through modern online educational platforms and learning tools.

Content module 1. Distance learning technologies
Topic 1. Forms of distance learning, platforms and technologies of distance learning (DL).
Topic 2. Psychological and pedagogical issues of DL. Principles, system and process of DL.
Topic 3. Goals and system of instructional design. The development phase of a distance course.
Topic 4. Requirements for a distance course. Structure of a distance course.

Content module 2. Information materials of a distance course and assessment in distance learning.
Topic 5. Information resources of a distance course (DC). Electronic educational resources.
Topic 6. Content design and visual design of a distance course.
Topic 7. Assessment and progress monitoring in DL.
Topic 8. Innovative methods and technologies of distance learning. Communication in distance learning.

Semester: Spring
ECTS credits: 4
Lecture hours: 20
Practical class hours: 20
Assessment: Credit (pass/fail)
Language of instruction: Ukrainian

Lecturer: Olha Kotova

The course “Probability Theory and Mathematical Statistics” develops fundamental knowledge of the patterns of random phenomena and methods of their mathematical analysis, which are essential for computer modelling of processes. The course covers the basic concepts of probability theory, sample spaces and conditional probability, random variables and their distributions, expected value, variance and other characteristics used to assess the uncertainty and variability of the systems studied. Particular attention is paid to approximation of distributions, the central limit theorem and methods of statistical data analysis widely used in modelling and analysing experimental data. Students study methods of estimating distribution parameters, constructing confidence intervals and testing statistical hypotheses, which are an integral part of making sound decisions under uncertainty. Special emphasis is placed on practical aspects: analysing real data with computer tools, modelling random processes with specialized software and interpreting research results. This approach allows students not only to learn the theory but also to master practical data analysis skills that are critical in developing mathematical models. The course develops critical thinking and the ability to draw statistically sound conclusions, assess the accuracy and reliability of modelling results and integrate knowledge from other areas of mathematics and computer science. It is the basis for further study of specialized modules in computer modelling, data analysis and forecasting, in line with the current demands of the job market and research.

Semester: Spring
ECTS credits: 4
Lecture hours: 20
Practical class hours: 20
Assessment: Credit (pass/fail)
Language of instruction: Ukrainian

Lecturer: Vitalii Kobets

Develop management and business planning skills in IT: from code to product management!

Do you dream not just of writing code, but of creating products that scale fast? Do you want to understand how to turn a client’s vague idea into a clear development plan? The course “Business Analysis for Software Product Development” is your bridge between technical implementation and business success.

Who is this course for? Year 2 students of Computer Science, Software Engineering and Information Systems who want to take on the roles of Project Manager (PM) or Product Owner (PO).

What will you learn? The course combines theory with real IT industry practice:

1. The art of requirements: you will learn to build the “foundation” of a project, from high-level business requirements (BRD) to detailed software requirements specifications (SRS). You will master User Stories and Acceptance Criteria so that developers know exactly what to build.

2. Project management: you will understand how work differs in product and outsourcing companies. We will study modern methodologies in detail, from classic Waterfall to agile Scrum and Kanban.

3. Artefacts: you will create a Project Charter yourself, learn to build Use Case diagrams and model real business processes.

4. Resource and risk management: you will learn to calculate budgets, estimate timelines using PERT, build Gantt charts and manage risks to avoid project failure.

Why choose this course in 2026–2027?

• Lecturer: Doctor of Economics, Professor Vitalii Kobets, an expert who combines academic depth with practical cases of digital transformation.
• Practical result: your final assignment will be a complete documentation package for your own start-up.
• Career start: you will gain a foundation for certifications and Junior PM/BA positions.

Don’t just code — manage the process of creating innovations! Choose Business Analysis and become leaders who know WHAT and HOW to build.

Semester: Spring
ECTS credits: 4
Lecture hours: 20
Practical class hours: 20
Assessment: Credit (pass/fail)
Language of instruction: English

Lecturer: Olha Hniedkova

The aim of the course is for students to achieve professional communicative competence in the main areas of their future professional activity in English: to develop the skills of using English in professional communication with people from other countries on various IT issues and in preparing for international conferences, projects and discussions, and to teach students to exchange business information orally and in writing. It also aims to develop practical skills in various types of speech activity within the topics required by their future personal, public, professional and educational activity, and to improve these skills by working with the latest authentic materials. The course is practice-oriented and aims to develop students’ professional and functional communicative competence in English by integrating speech skills and language knowledge within thematic and situational contexts relevant to the student’s academic and professional fields.

Semester: Spring
ECTS credits: 4
Lecture hours: 20
Practical class hours: 20
Assessment: Credit (pass/fail)
Language of instruction: Ukrainian

Lecturer: Iryna Chernenko

The course “Image Processing and Multimedia Technologies” is your path to mastering digital content

Why choose this course?

• You will gain skills that employers in IT, design and media are “hunting” for
• You will learn to create professional, eye-catching visual content
• You will master technologies used everywhere from the film industry to medicine
• You will work with Python tools and libraries for media processing

In class you will learn to:

• Create, analyse and transform digital images
• Apply filters and object recognition algorithms
• Work with different formats and colour models
• Edit video and create special effects
• Control multimedia processing programmatically

The course is interdisciplinary and combines elements of computer graphics, artificial intelligence, signal processing and information technology!

Who is this course for?

• Future specialists in digital technologies
• Those developing their own creative projects
• Students who want to combine creativity with technical skills
• Future computer science teachers

Choose “Image Processing and Multimedia Technologies” — invest in the skills of the future!

Semester: Autumn
ECTS credits: 5
Lecture hours: 26
Practical class hours: 24
Assessment: Graded credit
Language of instruction: Ukrainian

Lecturer: Oleksandr Lemeshchuk
Practical classes: Iryna Chernenko

The course “Project Workshop” develops students’ practical skills in team development of software products or information systems within a real or near-real project. It brings together the knowledge gained during studies and allows students to apply it in practice — from idea to implementation.

Main focus areas of the course:

• defining the task and analysing the client’s requirements;
• planning project stages and assigning team roles;
• developing the architecture and choosing technologies;
• programming, testing and deployment;
• preparing technical and presentation documentation;
• public defence or demonstration of results.

Projects can cover various areas: web or mobile applications, databases, information systems, product prototypes, research solutions, AI components and more, depending on the students’ specialization and goals.

During the workshop, students learn to:

• work in a team using modern methodologies (Agile, Scrum);
• use project management tools (Jira, Trello, Git);
• communicate effectively within the team and with the instructor/client;
• solve problems that arise during development.

The “Project Workshop” helps students understand the realities of working in IT, prepare for future employment, build a portfolio and gain experience working on a complete product as a team.

Semester: Autumn
ECTS credits: 5
Lecture hours: 26
Practical class hours: 24
Assessment: Graded credit
Language of instruction: Ukrainian

Lecturer: Vitalii Kobets

In today’s business, digital marketing is a key tool for promoting IT products. If you want to learn to work effectively with the digital technology market, use analytics for decision-making and create unique marketing strategies for IT companies, this course is for you!

Why choose this course?

• Relevance: digital marketing (performance, brand and product marketing) is a component of success for both tech start-ups and large IT companies.
• Practical focus: you will learn to work with SEO, content marketing, social media, contextual advertising and email marketing.
• Tools and analytics: you will become familiar with Google Analytics, Google Ads and Facebook Ads, as well as data analysis methods for assessing the effectiveness of marketing campaigns.
• Career prospects: after the course you will be able to assess your potential for working in IT marketing departments, developing your own start-ups or becoming an independent consultant.
• Electronic learning resources: access to video lectures, screencasts, lecture notes, a glossary, a workbook and a final project.

What will you study?

• The role of digital marketing in promoting IT products in unit economics.
• Brand building and creating Go-To-Market marketing strategies.
• Working with advertising platforms and managing ad campaigns in the App Store and Google Play.
• Analysing user behaviour and optimizing the TOFU-MOFU-BOFU sales funnel and the “pirate” funnel.
• Using AI and cohort RFM analysis in digital marketing.

Who is this course for? It is useful for students planning a career in IT, business analytics, advertising and IT entrepreneurship. If you want to gain a competitive edge and expand your opportunities in the digital world, this course will be your starting point!

Don’t wait — choose “Fundamentals of Business and Start-up Preparation” and become an expert in promoting innovative solutions!

Semester: Autumn
ECTS credits: 5
Lecture hours: 26
Practical class hours: 24
Assessment: Graded credit
Language of instruction: Ukrainian

Lecturer: Andrii Topalov

The course “Cloud Computing” develops knowledge and skills in modern technologies for processing, storing and managing data in the cloud. It covers fundamental concepts, architecture and service models (IaaS, PaaS, SaaS), as well as practical aspects of deploying, administering and securing cloud infrastructure. The course examines the basic principles of virtualization, clustering and distributed computing. Particular attention is paid to public and private platforms such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP). Typical use cases of cloud services for business, research and software development are considered. Practical classes include building cloud application prototypes, configuring environments, and deploying databases, web services and microservice architectures in the cloud. The course gives students up-to-date competences that enable them to work effectively with cloud technologies, implement innovative IT solutions and take part in developing scalable, secure and accessible cloud services in real projects.

Semester: Autumn
ECTS credits: 5
Lecture hours: 26
Practical class hours: 24
Assessment: Graded credit
Language of instruction: Ukrainian

Lecturer: Maksym Vinnyk

The course “User Interface Development” develops students’ knowledge and practical skills in creating effective, convenient and aesthetically appealing interfaces for web and mobile applications. It helps students understand how users interact with a software product and how to make this interaction as intuitive, logical and comfortable as possible. Students learn the basics of UI/UX (User Interface / User Experience) design and usability principles, and study the laws of information perception, design systems, typography, colour harmony and work with visual and sensory interface elements. Considerable attention is paid to responsive design, accessibility, microinteractions and the role of prototyping in development. The practical part of the course includes work with modern design tools (Figma, Adobe XD, etc.) and implementing interfaces using HTML, CSS, JavaScript and frameworks such as React or Vue. Students go through the full UI development cycle — from defining the task, analysing the target audience and building user scenarios to creating interactive prototypes and implementing them as a live interface. The course encourages interdisciplinary thinking, combining elements of psychology, design, programming and analytics. It develops not only technical skills but also design sensitivity, attention to detail and the ability to think from the end user’s perspective. Mastering this course is an important step for future front-end developers, UI/UX designers, web engineers and other specialists working at the intersection of technology and human interaction.

Semester: Spring
ECTS credits: 5
Lecture hours: 26
Practical class hours: 24
Assessment: Graded credit
Language of instruction: Ukrainian

Lecturer: Nataliia Valko

A course for future mathematics and computer science teachers based on the online platform Tinkercad. Students will learn to create 3D models using Tinkercad’s intuitive interface. The course covers the main modelling tools: adding and combining geometric shapes, editing objects, grouping and alignment. Particular attention is paid to designing models suitable for successful 3D printing. Students will learn to take into account factors such as the orientation of the model on the print bed and the need for support structures and their optimal placement to minimize material use and simplify post-processing. The course examines how to ensure the strength and stability of printed objects through proper wall thickness, stiffening ribs and other structural elements. This knowledge will enable future teachers to create functional and durable teaching materials using 3D printing. A separate block is devoted to preparing models for printing: exporting to STL and understanding slicing parameters. The course also covers practical applications of 3D printing in education for visualizing mathematical concepts (geometric shapes, function graphs) and creating visual aids for lessons and pupils’ project work. The basic principles of 3D printers and printing safety are also studied.

Semester: Spring
ECTS credits: 5
Lecture hours: 26
Practical class hours: 24
Assessment: Graded credit
Language of instruction: Ukrainian

Lecturer: Andrii Topalov

The course develops students’ theoretical knowledge and practical skills in developing modern mobile and web applications. It covers the main principles of designing client-server systems, creating user interfaces, working with databases, APIs and network services. Students master modern programming languages, frameworks and tools for building responsive web applications and mobile apps for Android and/or iOS. Considerable attention is paid to software architecture, testing, security, performance optimization and usability (UI/UX). The practical part includes laboratory and project work in which students gain experience developing fully functional software products — from design to deployment and maintenance. As a result, students acquire the competences needed to develop, test and support mobile and web applications and to apply modern technologies in their professional IT work.

Semester: Spring
ECTS credits: 5
Lecture hours: 26
Practical class hours: 24
Assessment: Graded credit
Language of instruction: Ukrainian

Lecturer: Andrii Topalov

The course “Parallel and Distributed Computing. Cloud Services” studies the theoretical foundations and practical approaches to building efficient computing systems that provide high performance, scalability and reliability. Particular attention is paid to integrating traditional parallel and distributed architectures with modern cloud technologies. In the distributed systems part, students learn about network protocols, client-server and peer-to-peer architectures, routing mechanisms, resource management and fault tolerance. The course also covers the principles of cluster and grid systems and their use in science, industry and large IT infrastructures. In practical classes, students create their own parallel and distributed solutions, automate computing processes in the cloud and develop prototypes of high-performance applications. The course develops competences in high-performance and cloud computing, which are key in scientific research, big data engineering and building scalable digital services.

Semester: Spring
ECTS credits: 5
Lecture hours: 26
Practical class hours: 24
Assessment: Graded credit
Language of instruction: Ukrainian

Lecturer: Iryna Chernenko

The course “Design in the Digital Network” develops students’ knowledge and skills in visual design for the digital environment — in particular websites, mobile applications, social media, digital advertising and interactive platforms.

The course covers both technical and creative aspects of design in an environment of constant interaction with the network and the user.

Within the course, students:

• become familiar with the principles of graphic, interface and information design in a digital context;
• study the specifics of screen composition, colour theory, typography and responsive layout;
• master digital design tools: Figma, Adobe XD, Canva, Sketch, Photoshop and others;
• analyse current trends in UI/UX design, online branding, web aesthetics and visual communication;
• create multimedia, interactive and responsive designs for different platforms.

Particular attention is paid to:

• user behaviour scenarios;
• accessibility and usability principles;
• design that takes into account the social, cultural and technical context of the digital network.

Practical classes include projects in web design, visual identity for social media, interactive presentations, digital posters, responsive interfaces and data visualization.

As a result, students gain skills in creating visual content that works effectively and is well perceived in the digital network, and are ready to work in the dynamic environment of modern digital communications.

Semester: Spring
ECTS credits: 5
Lecture hours: 24
Practical class hours: 26
Assessment: Graded credit
Language of instruction: Ukrainian

Lecturer: Serhii Babichev

Aim: to introduce students to the basics of modelling, programming and controlling industrial robots in the RoboDK virtual environment, and to develop practical skills in creating 3D models of robotic systems and developing and verifying robot control programs, taking into account kinematic and technological parameters. Objectives: to study the basic concepts and components of robotic systems; to become familiar with RoboDK’s features: the robot library, importing CAD parts and creating trajectories; to master the method of building a digital twin of a robotic manipulator; to learn to create and test motion programs for typical tasks (welding, painting, moving parts, etc.); to learn the basics of generating code for specific robot models (Fanuc, KUKA, ABB, etc.); to become familiar with integrating RoboDK with Python for advanced control and automation. As a result, students will know the principles of robot control programs, the architecture and interface of RoboDK, and methods of simulating and optimizing robot motion; and will be able to create a 3D model of a work cell in RoboDK, program typical technological operations for industrial robots, export and verify programs for physical robots, and use Python to automate processes in RoboDK.

Semester: Spring
ECTS credits: 5
Lecture hours: 26
Practical class hours: 24
Assessment: Graded credit
Language of instruction: Ukrainian

Lecturer: Dmytro Klonov

The aim of the course “Artificial Intelligence and Neural Networks” is to develop a system of concepts, knowledge and skills, including the basics of intelligent data analysis and the design, training and deployment of artificial neural networks. The course aims to give students an understanding of the key principles of machine learning algorithms and the use of modern tools, libraries and frameworks to solve applied problems of automation, forecasting and pattern recognition. Course summary: the course focuses on a thorough understanding of current trends in intelligent systems, the main neural network architectures (deep, convolutional and recurrent), data preprocessing methods, and practical skills in using Python and specialized tools to create, optimize and test AI models. The main objectives of the course are:

• familiarization with the theoretical foundations of artificial intelligence, classical machine learning algorithms and the mathematics of neural networks;
• familiarization with the principles of building, training and validating deep learning models;
• practical skills in using modern software kits, libraries and frameworks (such as TensorFlow, PyTorch, Keras, Scikit-learn);
• skills in preparing, cleaning and normalizing datasets;
• mastering tools for creating and configuring neural network architectures for classification, regression, clustering and natural language processing (NLP) tasks;
• practical skills in integrating trained AI models into real software systems and information systems.

The course belongs to the professional and practical training cycle. Knowledge of this course is currently one of the most important indicators of the qualifications of a computer technology and software engineering specialist. Studying it requires focused work with specialized literature, active participation in lectures and practical classes, independent study and individual assignments. “Artificial Intelligence and Neural Networks” is an elective course. It builds on Higher Mathematics (in particular linear algebra and probability theory), Programming, Object-Oriented Programming, Algorithms and Data Structures, Database Organization and Data Analysis, and supports subsequent coursework, qualification and Master’s theses.

Semester: Spring
ECTS credits: 5
Lecture hours: 26
Practical class hours: 24
Assessment: Graded credit
Language of instruction: Ukrainian

Lecturer: Volodymyr Peschanenko

The course “Formal Methods of Program Specification, Verification and Optimization” introduces students to mathematical approaches to the formal description, correctness checking and improvement of software. These methods are especially important for building reliable and secure systems where errors can have critical consequences — in finance, healthcare, aviation, robotics and other fields. The course covers: formal program specifications (how to describe the behaviour of a software system precisely in mathematical terms before implementation); program verification (logical methods for proving program correctness, including invariants, pre- and postconditions and Hoare logic); modelling and analysis of program states using automata, transition graphs and temporal logic; and methods of optimizing program code based on formal properties (eliminating redundant computations, optimizing resources, controlling complexity). The course develops abstract thinking, attention to precise formulation, analytical skills and the ability to work with critical aspects of software engineering. It is a foundation for further work in secure software development, cybersecurity, embedded systems and formal code analysis.

Semester: Spring
ECTS credits: 5
Lecture hours: 26
Practical class hours: 24
Assessment: Graded credit
Language of instruction: Ukrainian

Lecturer: Olha Hniedkova

The informatization of various spheres of life has led to qualitative changes in the use of audio and video equipment through multimedia and distance learning technologies, and distance technologies are increasingly used in education. During the course, students gain an understanding of the current possibilities of distance technologies in education and their use in a teacher’s practice, as well as the ability to use computer equipment correctly and consciously and to make rational use of computer technologies to create and implement computer-based support for the learning process and the teacher’s academic and methodological work.

The aim of the course “Methods and Technologies of Creating a Distance Course” is for students to master a system of knowledge and skills that enable them to perform the functions of distance learning, engage in high-quality professional and pedagogical communication, and achieve self-realization and self-improvement through verbal and non-verbal means of communication.

Semester: Spring
ECTS credits: 5
Lecture hours: 26
Practical class hours: 24
Assessment: Graded credit
Language of instruction: Ukrainian

Lecturer: Hennadii Kravtsov

The aim of the course “Virtual and Augmented Reality Technologies” is to develop a system of concepts, knowledge and skills, including basic knowledge of web programming with virtual and augmented reality multimedia technologies, and an understanding of the key principles of developing multimedia applications and using modern tools and technologies. Course summary: the course focuses on a thorough understanding of current trends in internet services, the main standards of data exchange between internet applications and services, methods of designing websites and services, and practical skills in using C# and JavaScript to create interactive multimedia applications and internet services. The main objectives of the course are:

• familiarization with the theoretical foundations of modern augmented and virtual reality technologies
• familiarization with the principles of developing augmented and virtual reality applications
• practical skills in using development environments and software development kits (SDKs)
• skills in working with Vuforia
• mastering tools for creating 2D and 3D applications with virtual reality support on the Unity platform
• practical skills in modelling and developing augmented and virtual reality applications.

The course belongs to the professional and practical training cycle. Knowledge of this course is currently an important indicator of a computer technology specialist’s qualifications. Studying it requires focused work with specialized literature, active participation in lectures and practical classes, independent study and individual assignments. “Virtual and Augmented Reality Technologies” is an elective course. It builds on Programming, Object-Oriented Programming, Algorithms and Data Structures, Web Programming, Web Design, Image Processing and Multimedia Technologies, and Mobile and Web Application Programming, and supports subsequent coursework, qualification and Master’s theses.