Bachelor of Science (B.Sc.) in Computer Science

Target Group

This programme is designed for a diverse target group who possess IT-related education and English proficiency. To manage the wide range of prior competence inherent in this group, all students must complete a Diagnostic Phase upon enrolment. Ascencia Malta offers the Ascend Programme that helps students prepare via the Ascend Digital Skills bridging course if they need additional preparation before the programme starts. This ensures foundational knowledge is standardised, preventing issues with varying skill levels in core modules.

Entry Requirements

B2-level English proficiency (IELTS 6.0, TOEFL iBT 78, or equivalent), plus post-secondary qualifications in Maths and/or Computing, or an MQF/EQF Level 6 degree with relevant qualifications, or a minimum of 2 years of IT-related professional experience (subject to approval).

The BSc in Computer Science programme prepares students for the following occupations:

• Software Engineer / Developer
• Systems Architect / Engineer
• Data Scientist / Data Analyst
• Machine Learning / AI Engineer
• Cybersecurity Specialist / Analyst
• Cloud Solutions Architect / DevOps Engineer
• IT Consultant / Technical Project Manager
• Product Manager
• Quality Assurance / Test Engineer
• Research Scientist / Academic

The programme is designed around a two-tiered professional preparation model ensuring that the required 180 ECTS covers both the general scope of the field and provides the necessary depth for high-level specialist roles, in compliance with MQF Level 6 requirements.

Knowledge and Understanding

Upon completion of this programme, learners will have acquired knowledge and understanding of:

• Basics of propositional and predicate logic.
• Logical connectives, truth tables, and quantifiers.
• Formal proofs, logical reasoning, and set theory.
• Python syntax, data types, and control structures.
• Functions, recursion, and object-oriented programming basics.
• Error handling, file manipulation, and working with libraries.
• Von Neumann architecture, CPU, memory, and I/O.
• Process management, threading, and synchronization.
• File systems, virtual memory, and basic shell scripting.
• Relational database concepts (tables, primary/foreign keys).
• SQL queries: SELECT, INSERT, UPDATE, DELETE, JOIN.
• ACID properties, normalization, and indexing.
• Research methodologies and academic writing structure.
• How to critically evaluate and summarize scientific papers.
• Citation styles, referencing, and plagiarism avoidance.
• Software development life cycle (SDLC).
• Agile methodologies, version control (Git), and testing.
• UML diagrams and software design principles (SOLID).
• Time and space complexity (Big-O notation).
• Sorting (MergeSort, QuickSort), searching (binary search).
• Data structures: linked lists, stacks, queues, trees, graphs.
• OOP principles: encapsulation, inheritance, polymorphism.
• Abstract classes, interfaces, and design patterns.
• Exception handling and memory management in OOP.
• Combinatorics, permutations, and probability.
• Graph theory: adjacency lists, shortest path algorithms.
• Boolean algebra, finite automata, and formal languages.
• OSI, TCP/IP models, network protocols.
• Routing, switching, and wireless networking concepts.
• Basics of distributed computing and fault tolerance.
• HTML, CSS, JavaScript, and front-end frameworks (React, Vue).
• Backend development with Node.js, Flask, or Django.
• RESTful APIs, authentication, and web security concepts.
• Principles of user-centered design and usability testing.
• Prototyping, wireframing, and accessibility guidelines.
• Cognitive psychology in HCI and human perception models.
• Supervised, unsupervised, and reinforcement learning.
• Neural networks, deep learning, and natural language processing.
• AI ethics, bias in machine learning, and real-world applications.
• NoSQL databases (MongoDB, Cassandra, Firebase).
• Big Data frameworks (Hadoop, Spark) and ETL processes.
• Data warehousing, indexing, and distributed database management.
• Cybersecurity principles, threat modeling, and cryptography.
• Penetration testing, malware analysis, and secure coding practices.
• Compliance with cybersecurity regulations (GDPR, ISO 27001).
• Basics of multithreading, parallel algorithms, and GPU computing.
• Cloud service models (IaaS, PaaS, SaaS) and virtualization.
• Serverless computing, Kubernetes, and microservices architecture.
• Industry-based problem-solving and software development.
• Application of software engineering methodologies.
• Technical documentation, testing, and deployment.
• Computer Vision: Image processing, feature extraction, object detection.
• Blockchain and Cryptocurrencies: Distributed ledger technologies, consensus mechanisms, and smart contracts.
• Robotics: Autonomous systems, sensor fusion, motion planning.
• Advanced Software Engineering: DevOps, CI/CD, large-scale software architecture, and microservices.

Skills

Upon completion of this programme, learners will have acquired the following skills:

• Construct and evaluate logical arguments.
• Apply formal proofs and truth tables in problem-solving.
• Use propositional and predicate logic in computing applications.
• Write clean, efficient Python programs.
• Debug and troubleshoot code effectively.
• Implement basic algorithms and data structures.
• Analyze CPU performance and memory management techniques.
• Use basic shell scripting for process automation.
• Manage file systems and OS-level configurations.
• Design and normalize relational database schemas.
• Write and optimize SQL queries for data retrieval and management.
• Perform basic database administration tasks.
• Critically analyze and summarize academic papers.
• Write structured technical reports and research papers.
• Use proper citation and referencing techniques.
• Apply Agile development methodologies in projects.
• Use Git for version control and collaborative development.
• Implement software design principles in coding projects.
• Implement efficient sorting and searching algorithms.
• Optimize code using appropriate data structures.
• Analyze time and space complexity of algorithms.
• Develop modular, maintainable applications using OOP principles.
• Use design patterns to improve software architecture.
• Implement exception handling and unit testing.
• Solve problems using combinatorial techniques.
• Apply graph theory to networking and pathfinding problems.
• Use Boolean algebra to design digital circuits.
• Configure basic network protocols and troubleshoot connectivity issues.
• Develop simple client-server applications.
• Implement basic distributed computing solutions.
• Build responsive web applications using HTML, CSS, JavaScript.
• Develop RESTful APIs and handle authentication.
• Optimize front-end and back-end performance.
• Conduct user research and usability testing.
• Create wireframes and prototypes using design tools.
• Implement accessibility and UX best practices in web applications.
• Develop machine learning models using leading frameworks such as TensorFlow and PyTorch.
• Preprocess and analyze datasets for AI applications.
• Optimize and fine-tune deep learning models.
• Manage and query large-scale NoSQL databases.
• Work with big data processing frameworks like Hadoop and Spark.
• Implement efficient indexing and distributed storage solutions.
• Perform penetration testing and vulnerability assessments.
• Implement cryptographic techniques to secure applications.
• Analyze and mitigate cyber threats in real-world scenarios.
• Develop parallel algorithms for high-performance computing.
• Deploy and manage cloud-based applications on AWS, Azure, or Google Cloud.
• Use containerization (Docker, Kubernetes) for scalable applications.
• Design and implement a full-scale software or AI-based system.
• Follow best practices in testing, deployment, and project documentation.
• Present and defend project outcomes to a technical audience.
• Computer Vision: Implement image recognition and object detection models.
• Blockchain and Cryptocurrencies: Develop and deploy functional smart contracts on a distributed ledger platform.
• Robotics: Program robotic systems for autonomous operations.
• Advanced Software Engineering: Apply CI/CD pipelines and DevOps practices.

General Pedagogical Methods

In-Person Learning: Sessions feature traditional lectures covering theoretical principles, terminology, and best practices. These sessions use multimedia presentations, real-world examples, and case studies to engage students actively. Students participate in tutorials, project work, hackathons, and competitions, which foster a collaborative and dynamic learning environment.

Online Learning: The online delivery model rigorously accounts for contact hours through a verified blend of mandatory synchronous live tutorials and asynchronous, tutor-led personalised feedback on all student submissions. Recorded video lectures deliver core content, accompanied by individual exercises designed for self-paced learning, with email support for any questions or challenges encountered during the online learning process.

General Assessment Methods

A variety of assessment methods are employed to allow learners with different learning styles and abilities to complete the programme successfully. Students prepare individual and team reports and presentations (where specified), apart from written and multiple-choice examinations. Most modules have a heavy assignment component which varies from term papers to implementing algorithms stemming from the unit.

Formative assessments include quizzes, class discussions, and draft submissions. Summative assessments include assignments, examinations, projects, and presentations. Assessments are designed to test both theoretical understanding and practical application of concepts.

Grading and Progression

For each module, students are required to achieve the minimum pass mark. Students who fail the module have an opportunity to resit. Should the student fail a second time, they will need to repeat the complete module.

To be promoted to the following year, students must have passed all the previous year credits. The Capstone Project is the terminal, 10-ECTS module of the programme, specifically designed to validate the student's final attainment of all programme learning outcomes and demonstrate independent operation at MQF Level 6 autonomy.

Bachelor of Science (BSc) in Computer Science — MQF/EQF Level 6, 180 ECTS

To complete the Bachelor of Science (BSc) in Computer Science, the student must successfully validate all 18 compulsory modules (170 ECTS) and one elective module (10 ECTS) from the Year 3 elective pool.