Duration
30h Th, 8h Labo., 30h Proj.
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This course aims at delivering a first vision of how data is processed and organized by large network organizations. In particular, it focuses on data and network traffic management within a data center (a facility composed of networked computers and storage used to organize, process, store and disseminate large amounts of data), wireless and mobile networks, and infrastructures for multimedia content delivery to users.
Table of Content:
Part 1: Data Center Networking (B. Donnet, L. Mathy)
Part 2: Wireless and Mobile Networks (G. Leduc)
Part 3: Multimedia Networking (G. Leduc)
Learning outcomes of the learning unit
Upon completing this course, students will understand modern network infrastructures (Data centers, wireless and mobile networks), as well as the way multimedia content can be delivered to end-users.
This course contributes to the learning outcomes I.1, I.2, I.3, II.1, II.2, III.1, III.3, III.4, IV.1, IV.2, VI.1, VI.2, VII.1, VII.2, VII.4, VII.5, VII.6 of the MSc in data science and engineering.
This course contributes to the learning outcomes I.1, I.2, II.1, II.2, III.1, III.3, III.4, IV.1, IV.2, IV.3, IV.4, VI.1, VI.2, VII.1, VII.2, VII.4, VII.5, VII.6 of the MSc in computer science and engineering.
Prerequisite knowledge and skills
A good knowledge of basics of Computer Networking (INFO0010 or equivalent) is required.
Being comfortable with programming (e.g., Python) is also suitable.
Planned learning activities and teaching methods
The course is organized as follows:
- Lectures describing in details the theoretical and practical concepts of the course
- Lab sessions (4 sessions over the semester) are supervised practical sessions. Students are expected to prepare, at home, those sessions by making sure all requirements are installed on their own computer. Labs are done individually and a short report (simple text file to fill in and/or piece of code) must be completed by the end of the lab session.
- An assignment by teams of 2 students,i.e., a larger problem to be solved.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Further information:
Face-to-face course
Additional information:
The face-to-face lectures are complemented by lab sessions, an introduction to Python libraries (Panda, Matplotlib) and several seminars. The assignments are carried out remotely.
The course is entirely given in English.
Course materials and recommended or required readings
Slides, labs and assignments subjects will be made available on eCampus.
Following books have been used for building the course:
- J. Kurose, K. Roth. Computer Networking: A Top-Down Approach, 8th edition, Pearson, 2020.
Exam(s) in session
Any session
- In-person
oral exam
Further information:
Exam(s) in session
Any session
- In-person
oral exam
Additional information:
Students are graded in two ways: continuous evaluation (40% of the final grade) and oral exam (60% of the final grade).
Continuous Evaluation
During the semester, students will be evaluated several times through practical sessions:
- Lab reports. Several labs are organized during the semester and are graded (20% of the final grade) through reporting (a simple text file to fill in during/just after the lab with student's answers).
- Assignment. One assignment is organized during the semester and is graded (20% of the final grade).
Presence at the labs is mandatory. Attending all labs and doing all assignements is required for attending the oral exam. In case of lab absence and/or assignement not provided, the student will receive an "Absence" grade (and automatically be postponed to the resit).
Oral Exam
The oral exam (on the theoretical part of the course) accounts for 60% of the final grade.
Resit
In case of failure in June, students must improve their assignement for the resit (deadline 1st day of the resit session) if the grade was below 10/20. This must be done individually (note that no support will be provided, either by the TA or the lecturers, during summer). Labs cannot be redone.
If the grades of the labs are favorable to the students, the resit session is identical to the first one, with the same weighting. On the other hand, if the labs grades are detrimental to the student, it will not be taken into account in the weighting in September.
Oral exam must be redone.
Work placement(s)
Organisational remarks and main changes to the course
The course is organized during the first term (from mid-September to end of December), on Friday afternoon. All lectures are in English.
Contacts
Professors:
- Benoit Donnet (mail -- office 1.87b/B28)
- Guy Leduc (mail -- office 1.73a/B28)
- Laurent Mathy (mail -- office 1.15/B37)
- Émilien Wansart (Emilien.Wansart@uliege.be)
- Maxime Goffart (Maxime.Goffart@uliege.be)
Association of one or more MOOCs
Items online
Course Web Site
The course web site contains PDF of the slides, labs/assignments subjects, details about gradings, and the course agenda. It also allows students to interact with the Pedagogical Team through the Discussion forum.