Networks and Distributed Systems

Explore the programs and courses offered by Networks and Distributed Systems

Browse Programs Admission Information

Program Overview

Semester 1:

  • BDR: Distributed Databases
  • RSA: Autonomous Networks and Systems
  • AAC: Advanced Algorithms and Complexity
  • SR: Distributed Systems
  • PWA: Advanced Web Programming
  • ML: Machine Learning
  • ANG1: English 1
  • CDT: Corruption and Work Ethics

Semester 2:

  • ACS: Client/Server Administration
  • AR: Distributed Algorithms
  • RA: Advanced Networks
  • RM: Mobile Networks
  • EPS: System Performance Evaluation
  • DL: Deep Learning
  • ANG2: English 2
  • EL: E-learning

Semester 3:

  • APR: Development of Parallel and Distributed Applications
  • TOR: Network Optimization Techniques
  • SRE: Network Security
  • P2PB: P2P and Blockchain
  • SCC: SDN and Cloud Computing
  • CPD: Parallel and Distributed Computing
  • PIS: Specialized Programming
  • MR: Research Methodology

Semester 4:

Final Year Project (Internship in a Company)


Teaching Language : French and English

Curriculum Highlights

Core Courses

  • BDR: Distributed Databases
  • RSA: Autonomous Networks and Systems
  • AAC: Advanced Algorithms and Complexity
  • SR: Distributed Systems
  • PWA: Advanced Web Programming
  • ML: Machine Learning
  • ANG1: English 1
  • CDT: Corruption and Work Ethics
  • ACS: Client/Server Administration
  • AR: Distributed Algorithms
  • RA: Advanced Networks
  • RM: Mobile Networks
  • ANG2: English 2
  • EL: E-learning


Advanced Topics

  • EPS: System Performance Evaluation
  • DL: Deep Learning
  • APR: Development of Parallel and Distributed Applications
  • TOR: Network Optimization Techniques
  • SRE: Network Security
  • P2PB: P2P and Blockchain
  • SCC: SDN and Cloud Computing
  • CPD: Parallel and Distributed Computing
  • PIS: Specialized Computer Programming
  • MR: Research Methodology


View Full Curriculum

Admissions Information

In accordance with Articles 171 and 1023 of the relevant decrees:

  • Competencies and knowledge are assessed every six months through continuous assessment and a final exam.
  • Progression from the first to the second year is automatic upon successful completion of the first two semesters of the training program.
  • Student evaluation is based, according to the training program, on lectures, practical work, tutorials, and internships.


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