Bachelor's in Computer Science

Introduction to Computer Science

Overview: Learn fundamental computer science concepts, including algorithmic thinking, computer networks, and computational logic.

Skills: Develop problem-solving abilities using programming languages and logical structures.

Real-world application:

• Design and implement small automation scripts to streamline everyday tasks, such as processing large sets of customer data in Excel using Python.

• Develop a simple chatbot that responds to basic user queries, helping businesses automate customer service.

• Create a program that organizes and sorts large datasets, useful for companies managing inventory, finances, or user records.

Object-oriented Programming with Java

Overview: Understand software engineering principles using Java, including encapsulation, inheritance, and polymorphism.

Skills: Write clean, scalable, and efficient code while structuring complex applications.

Real-world application:

• Build a real-time weather application that fetches data from an API and displays it dynamically.

• Develop a basic e-commerce backend system that manages orders, inventory, and payments.

• Create a multiplayer game engine where players can interact in real-time, applying object-oriented principles to manage game mechanics.

Web Application Development

Overview: Learn the principles of web technology and full-stack development using modern frameworks.
Skills: Create interactive, responsive websites with HTML, CSS, JavaScript, and server-side scripting.
Learning outcomes: On successful completion, students will be able to:

• Identify important elements and describe the structure of current web application architectures.

• Write simple static web pages using HTML.

• Design simple web pages using CSS.

• Store and handle structured information using XML.

• Programme simple dynamic web content using PHP.

Computer Architecture and Operating Systems

Overview: Explore how computer architecture and operating systems interact, including memory management and CPU performance.
Skills: Understand system programming, process scheduling, and system optimization.
Learning outcomes: On successful completion, students will be able to:

• Explain the basic concepts of computer architecture.

• Compare the different types of computer hardware.

• Describe the meaning and functionality of simple assembly programmes.

• Explain the basic functions of operating systems.

Database Modelling and Database Systems

Overview: Develop a comprehensive understanding of relational and NoSQL databases and acquire hands-on experience in data modeling and query languages.
Skills: Design, implement, and manage data schemas; write and optimise SQL queries; and distinguish between relational and NoSQL database systems.
Learning outcomes: On successful completion, students will be able to:

• Describe the basic concepts of the relational data model and distinguish them from each other.

• Write and execute SQL queries to read data from databases, modify database content, and gain practical experience in their use.

• Design, create, and modify SQL queries and data schemas for SQL databases, and apply them effectively.

• Independently design database schemas and formulate queries to solve concrete problems.

  
  

IT Service Management

Overview: Gain insight into the principles, frameworks, and operational challenges of IT service management with an emphasis on industry standards such as ITIL and IT security management.
Skills: Understand, apply, and evaluate IT service management frameworks and practices, and address IT security challenges within organisations.
Learning outcomes: On successful completion, students will be able to:

• Identify the fundamentals and challenges of IT service management.

• Describe the motivation and structure of the IT Infrastructure Library (ITIL), distinguish its four dimensions, apply the service value system, and identify concrete practices.

• Describe and apply the fundamentals of IT security management.

Theoretical Computer Science and Mathematical Logic

Overview: Explore foundational theories of computation, formal languages, and the mathematical logic underlying programming and algorithms.

Skills: Formulate logical relationships, analyse computational models, and apply advanced concepts in computability and complexity.

Learning outcomes: On successful completion, students will be able to:

• Formulate and translate predicate logical relationships into programming languages.

• Use finite automatons and regular expressions to describe technical facts.

• Explain the Chomsky hierarchy.

• Identify the limits of provability and predictability.

• Explain the meaning and relevance of the P=NP problem.

• Apply Petri nets for the description of technical facts.

  
  

Introduction to Programming with Python

Overview: Learn the basics of programming using Python, with a focus on syntax, data types, and practical problem-solving for data science.

Skills: Develop, test, and maintain simple programs in Python; understand common data structures and programming patterns.

Learning outcomes: On successful completion, students will be able to:

• Use fundamental Python syntax.

• Recollect common elementary data types.

• Recognise foundational programming concepts and their realisation in Python.

• Understand error handling and logging.

• Create working programs.

• List the most important libraries and packages for data science.

Introduction to Data Protection and Cyber Security

Overview: Gain an understanding of the core concepts, procedures, and legal standards in IT security and data protection, with a focus on practical application and management strategies.

Skills: Identify key terms and techniques in IT security, interpret legal regulations, and apply security management strategies to real-world scenarios.

Learning outcomes: On successful completion, students will be able to:

• Explain the terms and concepts of IT security and know the typical procedures and techniques which exist in each area.

• Cite the legal regulations on data protection and explain their implementation.

• Discuss in-depth IT security management and suitable measures for implementation.

• Use their overview knowledge of activities and strategies for IT security in software and system development.

Cryptography

Overview: Study the principles and practical applications of cryptographic systems, covering both symmetric and asymmetric methods and their relevance to modern IT security.

Skills: Classify cryptographic systems, describe core encryption and hashing techniques, and understand the practical scenarios where cryptography is applied.

Learning outcomes: On successful completion, students will be able to:

• Give an overview of different classes of cryptographic systems.

• Give a basic description of symmetric cryptographic methods, in particular One-Time Pad, DES, and AES, and describe their operating principles by means of simple, concrete examples.

• Describe the basic hash functions.

• Describe basic asymmetric cryptographic methods, especially RSA, and their operating principles by means of simple, concrete examples.

• Describe the areas of application of cryptographic procedures and their application scenarios.

Overview: Study the unique challenges, processes, and technical considerations in developing software for mobile systems, with a focus on the Android platform.

Skills: Analyse mobile development practices, differentiate technical architectures, and independently implement solutions for mobile applications.

Learning outcomes: On successful completion, students will be able to:

• Recognize the differences and peculiarities of software development for mobile systems and explain them.

• Differentiate between different activities, roles, and risks in the creation, operation, and maintenance of mobile software systems.

• Explain and differentiate between the architecture and technical features of the Android platform.

• Independently create mobile software systems to solve concrete problems for the “Android” platform.

Overview: Explore the landscape of big data, including data types and sources, data formats, distributed analysis systems, and the motivations behind modern data storage technologies.

Skills: Identify and handle various data types, evaluate distributed analysis and streaming frameworks, and understand the use of NoSQL data stores.

Learning outcomes: On successful completion, students will be able to:

• Name types and sources of data.

• Understand text-based and binary data formats.

• Analyze the requirements and constraints of distributed analysis systems.

• Evaluate the applications of streaming frameworks.

• Describe the motivation for NoSQL data stores and categorise pertaining established concepts.