Master 1

Computer Science for Networks


This program proposes students an initiation to industrial and academic research and allows them to acquire strong practical and theoretical knowledge in Computer Science and Networking. The broad range of proposed courses gives students the opportunity to develop excellent technical and scientific skills in Computer Science. The courses are given by distinguished world-class professors and experts in related areas.
M1 CSN enables the graduates to acquire the necessary background in both Computer Science and Networks, to continue for the second year in IP Paris or choose another major in France or abroad. Given a large spectrum of technical skills, the graduates are also ready to join the industry directly.

Language of instruction: English

ECTS: 60

Oriented: both, Research and Industry, depending on the foreseen step of M2 Master Studies

Duration: 1 year

Courses Location: Palaiseau

Educational objectives

M1 CSN targets the following two main complementary objectives:
1) To master formal techniques for communicating network analysis. The students will study novel techniques and tools to model and analyze complex (future) networks.
2) To master software engineering techniques to compute, improve and grasp the subtleties of distributed networks’ development.
A number of labs and projects are scheduled for students to practice and comprehend concepts more easily. High quality lectures and project supervision are provided by expert professors and technical stuff from industry.

Program structure

The M1 CSN program courses are arranged in two semesters (30 ECTS per semester).

The list of classes includes the following:

  • Principles of data management
  • Networks
  • Software and Data Engineering
  • Advanced data network
  • Probas & Stats
  • Network programming
  • C Programming and Unix
  • French Language and Culture (in each semester)
  • Low-level data management
  • Introduction to Information Theory
  • Effective Communications
  • Entrepreneurship
  • Optimisation
  • System and network administration
  • Performance evaluation and metrics
  • Engineering for quality of service
  • Introduction to Machine Learning
  • Project

Principles of data management

ECTS 2.5
Hours : Courses 9h, Practical work 6h, Tutorial classes 6h, homework 21h
Lecturer: Sophie Chabridon

This course is an introduction to the principles of data representation, management and manipulation.
The presented concepts and technologies include:

  • Data representation
  • Relational database design
  • Entity-Relationship Diagram (ERD) and data modeling with UML
  • Transforming ERDs into relational models
  • Discovery and manipulation of the Structured Query Language (SQL)
  • Transaction management
  • Comparison with NoSQL systems

Continuous assessment (graded homework) and final exam


Hours: 2 courses 7h, Labs 6h, integrated courses 12h, Homework 42h
Lecturers: Badii JOUABER (responsible) and other faculty members from IP-Paris

This course aims at providing advanced skills in computer networks. At the end of this course, students must be able

  • to set up a local computer network including sub clusters and gateways to Internet.
  • to configure IP addresses and rules to enable efficient data routing, in terms of reliability, quality and cost
  • to configure a set of network functions and layers according to different needs and to understand networking functions’ interdependence and interactions.
    And more globally to propose an efficient networking solution for small to medium size structures.


  • Fundamentals of Computer Networks
  • Architectural Models (TCP/IP, OSI)
  • Layers and Protocols
  • Mechanisms and functions for reliable data transfer
  • Networking technologies
  • Advanced protocols, mobility impacts and multi-homing solutions (Mobile IP, MTCP, LISP, …)

Labs results + Integrated courses results + Homework + final exam

Software and Data Engineering

ECTS 2.5
Hours: 12h courses, 9h practical work, 21h homework
Lecturer: J. Paul Gibson

Learning Objectives: Capacity to identify and describe the software life cycle, data management, roles, artifacts, and activities. Understand the concepts of software « best practices » and when they apply. Be able to adapt a software development process to ones needs and select an appropriate set of best practices that will guide you in completing a software development project.

The assessment is a mix of continual assessment based on project work and a final exam

Advanced data networks

CM: 21h, TP: 21h, homework: 20h
Lecturer: Hossam Afifi

Learning objectives:

  • Acquire in-depth knowledge security protocols
  • Acquire a complete understanding of multimedia systems and infrastructures
  • Acquire a global view of virtualization principles and tools.

7 mandatory labs complement the theoretical view of each domain.

Continuous control

Probas & Stats

Hours: Courses 42h, homework 42h
Lecturer: François Simon


  • Finite probability spaces
  • Probabilities and measure theory (basics)
  • Random values
  • Moments of a R.V.
  • Introduction to experimental design & phrasing research questions
  • Introduction to R
  • Descriptive statistics
  • Normal distribution
  • Effect sizes & confidence intervals
  • Experimental designs

Final exam

Network programming

ECTS 2.5
Hours : Courses 6 hours, practical work 15h, homework 21h
Lecturer: Anis Laouiti

This course aims at acquiring skills in network programming using BSD sockets, including connected and disconnected modes, broadcasting and multicasting.

Final exam

C Programming and Unix

Hours : Courses 21h, Practical work 21h
Lecturers: Natalia Kushik and Jorge Lopez

This course aims at providing advanced skills in programming, in particular for the C programming language under Unix operating system.
This course includes:

  • Introduction to Unix: file system, process management, redirections and pipes, shell scripts, etc.
  • C programming language: compilation steps, data types and operations, functions, arrays and pointers, inputs/outputs, etc.
  • Algorithms and data structures (arrays, lists, trees, etc.)

Continuous assessment and final exam

French Language and Culture

ECTS 2,5
Hours: Courses 21
Lecturer: Nicoline Lagel

The class aims as developing language and cultural skills of international students.
Non-francophone students will take advantage of the presentation of various aspects of French culture.
The courses are arranged by language levels in accordance with CEFR framework.

Continuous assessment and final exam

Low-level data management

ECTS 2.5
Hours: courses 12h, Practical work 9h, homework 21h

This course aims at presenting the management of data and processes both in kernel and user spaces and their access using drivers and system calls. This includes in-kernel memory and data management, pipes and shared-memory and low-level messaging.

Final exam

Introduction to Information Theory

Hours: Courses 8h, tutorial classes 7h
Lecturer: François Simon

This course is an introduction to Information Theory. Fundamental
Concepts (Information Source, Entropy, Conditional Entropy, Mutual
Information) and classical applications (Compression, Reliable
Communication) are introduced and illustrated by examples and exercises.
Content of the course:

  • Model of Information Source
  • Information Measures and their relationships: Entropy, Condition
  • Entropy, Mutual Information
  • Shannon First Theorem and Data Compression (Huffmann Algorithm, Lempel Ziv Algorithm)
  • Communication channel
  • Shannon Second Theorem and Reliable Communication
  • First example of error correcting codes: Hamming codes

Final exam

Effective Communications
ECTS 2.5
CM : 21h, homework : 21h
Lecturer: Shirley Thomas
Brief program description
We aim to consolidate academic study skills and in particular equip students with the communicative skills& practices required of a doctoral student and a professional engineer. Practice is provided in the following: oral presentations, academic writing and inter-cultural awareness. This module does include preliminary lectures but the majority of class time is spent on interactive exercises, discussion and hands-on experience.
Preparation and follow-up assignments encourage students to draw conclusions from personal experience inside and outside the classroom.
Continuous assessment

Lecturers: Walid Benameur and José Neto
Brief program description
The content will be disclosed soon.

Introduction to Machine Learning
ECTS 2,5
Lecturer: TBD (Professor from TSP is foreseen)
Brief program description
The content will be disclosed soon.

Engineering for quality of service
ECTS 2.5
Cours : 12h, TDs : 9h, homework : 21h
Lecturer: Tijani Chahed
Brief program description
Students following this course will learn to master the different performance-related engineering approaches, such as dimensioning, resource allocation and control of wired as well as wireless networks. These techniques and tools shall enable them to design QoS-capable networks that fit best target applications and services, including :

  • Dimensioning (In circuit-switched and paquet switched networks, wired and wireless)
  • Resource allocation (Efficiency versus fairness considerations)
  • Congestion control (TCP-related, Active Queue Mangement, etc.)
  • QoS mechanisms (insterv, diffserv, MPLS)

Continuous assessment and presentations

System and network administration

Hours: Courses 21h, Tutorial classes 21h
Lecturer: Djamal Zeghlache and Jorge Lopez

This course aims at providing students knowledge and tools for creating and managing basic services / applications. The student, after successful completion of this course should be capable of implementing and managing all network services for an institution.

The course material includes:

  • Basic *-nix Server administration: networking system, installing / upgrading software, account provisioning, access control, backups, local documentation, troubleshooting, basic security, booting & shutting down, etc.
  • Specific services: Network Monitoring Systems, Dynamic Host Configuration Protocol, Domain Name System, Simple Mail Transfer Protocol, Hyper Text Transfer Protocol, Proxy / caching web server, Storage, etc.
  • Perl scripting for system administration
  • Virtualization: virtual machines, containers, virtualization managers and service descriptors, cloud computing services and APIs.

Continuous assessment and final exam

Performance Evaluation and Metrics

ECTS 2,5
Hours: Courses 21h, homework 21h
Lecturer: Michel Marot

The course intends to give the students the ability to understand problems related with the stochastic nature of the flows modern communication networks are dealing with. It introduces the mathematical tools to be deployed, and which are built on classical Probability Theory and are referred to as « Queuing Theory ».

Final exam


ECTS 2.5
Hours: Courses 12h, homework 30h
Lecturer: Natalia Kushik

The course aims at giving an insight on how research projects are implemented in an academic environment. As part of the course activities, the students attend research seminars as well as students’ presentations (e.g., PhD / Master thesis defenses).

Continuous assessment

Involved laboratories

Career prospects

CSN Master Program leads to research and engineering positions in the fields of modelling and analysis of complex networks and distributed computing for advanced communicating systems.
Industrial key players in the area of networking, such as Nokia, Orange, Huawei, etc. actively hire the CSN graduates. At the same time, CSN Master offers various PhD opportunities either in one of the Labs of IP Paris or in other leading National or International Universities / Research Centers.
CSN at the M1 level enables the graduates to acquire the necessary background in both Computer Science and Networks, to continue for the second year within one of the programs offered at IP Paris or choose another major in France or abroad.

Institutional partners

  • TSP is an active member of ERASMUS program
  • European ERASMUS Universities take part in the academic exchanges within M1 CSN

Industrial partners

  • Nokia
  • Orange
  • Huawei
  • Thales
  • Airbus
  • IT industry players


Application guidelines for a master’s program at IP Paris

Academic prerequisites

  • A Bachelor of Science degree or equivalent is required.
  • Applicants are required to have good mathematics and networks foundations
  • Basic knowledge in algorithms, software engineering and general network architectures is needed.

Language prerequisites

  • Expected English level – B2 or higher (CEFR)
  • For non-English speakers, certificates proving the levels are mandatory

Application timeline

Deadlines for the Master application sessions are as follows:
– First session: February 28, 2020
– Second session: April 30, 2020
– Third Session (optional): June 30, 2020 (only if there are availabilities remaining after the 2 first sessions)
Applications not finalized for a session will automatically be carried over to the next session.

You shall receive an answer 2 months after the application deadline of the session.
You can check your application status by logging in your candidate space.

Tuition fees

International Master: EU students : 4250 euros / Non-EU students: 6250 euros