*Program Overview*

- *Credits*: 60–120 ECTS or 36–48 semester credits. 

- *Structure*: Core courses + electives + thesis/capstone project. 

- *Prerequisites*: Bachelor’s in CS or related field (may require math/programming proficiency).

 

*Core Courses* 

(Mandatory foundational subjects) 

 

1. *Advanced Algorithms & Data Structures* 

   - Algorithm design (greedy, dynamic programming). 

   - Complexity analysis (time/space, NP-completeness). 

   - Graph algorithms, parallel/distributed algorithms. 

 

2. *Operating Systems & Distributed Systems* 

   - Kernel design, virtualization, concurrency. 

   - Cloud computing, distributed consensus (e.g., blockchain). 

 

3. *Machine Learning & Artificial Intelligence* 

   - Supervised/unsupervised learning, neural networks. 

   - NLP, computer vision, reinforcement learning. 

 

4. *Database Systems & Big Data* 

   - Relational/NoSQL databases, data warehousing. 

   - Hadoop/Spark, data mining, analytics. 

 

5. *Computer Networks & Cybersecurity* 

   - Network protocols (TCP/IP, SDN), IoT. 

   - Cryptography, intrusion detection, ethical hacking. 

 

6. *Software Engineering* 

   - Agile/DevOps, software architecture. 

   - Testing, maintenance, project management. 

 

7. *Research Methods in CS* 

   - Literature review, experimental design, academic writing. 

 

*Electives & Specializations* 

(Students choose 4–6 courses based on interests) 

 

*Artificial Intelligence*: 

- Deep Learning, Robotics, Cognitive Computing. 

 

*Data Science*: 

- Predictive Analytics, Data Visualization, Bayesian Methods. 

 

*Cybersecurity*: 

- Penetration Testing, Digital Forensics, Blockchain Security. 

 

*Cloud/Systems*: 

- Kubernetes, Edge Computing, Serverless Architecture. 

 

*Human-Computer Interaction (HCI)*: 

- UX Design, AR/VR, Usability Testing. 

 

*Quantum Computing*: 

- Quantum algorithms, Qubit programming. 

 

*Other Electives*: 

- Bioinformatics, Computer Vision, IoT, Game Theory. 

 

*Thesis/Capstone Project* 

- *Research Thesis* (12–18 credits): Original research under faculty guidance. 

- *Capstone Project*: Industry/client-based practical implementation (e.g., building a scalable app, AI model deployment). 

- *Dissertation Defence*: Presentation and evaluation. 

 

*Additional Requirements* 

- *Seminars/Workshops*: Attend talks on emerging trends (e.g., AI ethics, quantum supremacy). 

- *Internships*: Optional industry placements for hands-on experience. 

- *Comprehensive Exams*: Some programs require exams to assess core knowledge. 

 

*Sample Course Sequence* 

*Year 1*: 

- Semester 1: Advanced Algorithms, ML/AI, Operating Systems. 

- Semester 2: Databases, Networks, Elective 1. 

 

*Year 2*: 

- Semester 3: Electives 2–4, Research Methods. 

- Semester 4: Thesis/Capstone Project. 

 

*Assessment* 

- Exams, research papers, coding projects, presentations. 

- Thesis evaluated by a committee.