The general competencies that students will acquire are analysis, synthesis and predictions of solutions and consequences, mastery of research methods, procedures and processes as well as application of knowledge in practice. At the end of the course, a successful student is expected to master: a) recognition and understanding of applied techniques and approaches in data science and artificial intelligence b) formulation of problems from the domain of data science and artificial intelligence and practical implementation of simple solutions c) select and use appropriate software packages and libraries for simple analytics and visualization of data sets, as well as to apply a simple artificial intelligence algorithm to such a set.

After listening to this course, students will understand the limitations of machine learning methods in extracting relevant information from data. Students will gain practical insight into the fundamental trade-offs in learning from data and be able to recognize common pitfalls in the process of automated data analysis. Students will get acquainted with concrete techniques for data preparation and transformation and gain knowledge about the application of these techniques in practice. Students will be able to formulate a learning problem in terms of inputs and outputs, select a learning model or algorithm, and provide insight into the effectiveness of analysis based on the selected learning model.

After listening to this course, the student will be able to understand, evaluate and optimize the work of existing algorithms, to choose optimal algorithms and configurations for the analysis of specific data sets, as well as to develop their own algorithms and meta-algorithms in this domain. They master typical data structures, searching and sorting, dynamic programming, optimization techniques and graph algorithms, as well as their practical execution in a distributed computing environment.

The goal of this course is to enable students to acquire the necessary knowledge in the field of algebra and linear algebra in order to better understand material from other mathematical and vocational subjects. In addition, it is important that students master the basics of relational and operational algebraic structures used in programming and other professional fields. Also, students will be directed to use other professional resources and software that can help them solve problems in these areas.

After listening to this course, the student will be able to understand the characteristics of big data and typical problems in extracting relevant information from data. Students will gain practical insight into the fundamental trade-offs in learning from big data and be able to solve typical big data processing problems. Students will learn about specific methodologies and tools for processing big data.

This course aims to introduce students to advanced concepts and techniques in the field of databases, including transaction mechanisms, indexing, query optimization, and data security. In addition to relational databases, the course provides theoretical and practical knowledge of the principles, components and operational modules of modern non-relational and cloud databases.

The aim of this course is to provide students with a comprehensive understanding of the key concepts, principles and technologies used in cloud computing. The course focuses on advanced technologies and tools used in cloud computing, including virtualization, containerization, and orchestration, and explores their use in designing and implementing cloud-based solutions. The course focuses on a detailed understanding of the security risks and compliance requirements of cloud computing and applies best practices for securing cloud environments.

Understanding the role and ways of applying models of different levels of abstraction in software development. Mastering selected techniques and tools for modeling and specification of software components and systems.

Understanding the principles and techniques of software validation and verification and familiarization with the most significant problems, trends and results in the field.

One of the main goals of the course is to enable students to successfully apply deep learning methods, primarily artificial neural networks, to different tasks and to understand their applications and challenges in different fields. Another important goal of the course is to enable students to understand the mathematical background of deep learning methods, primarily backpropagation learning and optimization algorithms, such as the stochastic gradient descent (SGD) algorithm. Although the focus of the course is on classical neural networks, the so-called multilayer perceptrons, students will also be reminded of newer architectures such as convolutional and recurrent neural networks, autoencoders and transformers and their applications.

This course provides students with a deep understanding of the fundamentals, techniques and applications in the field of natural language processing. It includes the development of students' abilities to successfully deploy and implement various methods and algorithms in applications involving textual data. The main goal is for students to be able to analyze, model and solve problems using natural language processing techniques and thus contribute to the field with creative and effective solutions. By studying this subject, students also develop their critical thinking and communication skills, which prepares them for a successful career in this field.

Students will gain a comprehensive understanding of the principles and practices of software configuration management (SCM), enabling them to effectively manage software development projects, improve collaboration, and ensure software quality and reliability.

The aim of the course is to acquire theoretical and practical knowledge in the management of the entire life cycle of a software product with the aim of students mastering the basic principles, techniques and technologies of software product management.

The goal of the course is to enable students to apply their knowledge in solving real problems and challenges in the field of data science, such as social network analysis, trend prediction or analysis of large data sets.

Through study and research work, the candidate's independence is checked to perform a more complex and comprehensive analysis of a problem in the field of data science.

The goal of professional practice is for students to apply acquired knowledge and skills in solving specific problems in everyday practice.

In the final master's thesis, the student should demonstrate that he is competent to identify the problem, perform analysis and specify the model of a data science system of medium complexity level using methodological approaches and machine learning technologies, to implement it using current technologies and tools of software engineering, to critically evaluate the achieved results and to propose further directions of work.

In the final master's thesis, the student should demonstrate that he is competent to identify the problem, perform analysis and specify the model of a data science system of medium complexity level using methodological approaches and machine learning technologies, to implement it using current technologies and tools of software engineering, to critically evaluate the achieved results and to propose further directions of work.