5.00 credits
30.0 h + 30.0 h
Q1
Teacher(s)
Mens Kim; Nijssen Siegfried; Pecheur Charles;
Language
French
Main themes
- Fundamental concepts of object-oriented programming;
- Python programming language;
- Analysis of a computer problem, design, specification and implementation of a solution;
- Linear data structures.
Learning outcomes
At the end of this learning unit, the student is able to : | |
1 |
Given the learning outcomes of the "Bachelor in Computer science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
|
Content
- Programs, source code and program execution
- Identifiers, variables, values, types, assignment
- Expressions, statements
- Conditional structures and loops
- Functions, parameters, calls, results, execution, variable scoping
- Specifications and tests
- Modules
- Data structures, lists, strings and their operations
- References and nested data structures
- Nestsed lists, tuples, matrices, dictionnaries
- Dichotomic search algorithms
- File handling, input/output
- Exception handling
- Object-oriented programming and garbage collection
- Classes, objects, constructors, methods
- References to an object, self-references and self-calls
- Class, instance and local variables, scope and visibility
- Class composition, inheritance and encapsulation
- Polymorphism, super calls and dynamic binding
- Object equality
- Linked data structures
Teaching methods
The chosen teaching method relies on active student participation, through a mixture of :
- course lectures,
- partical exercice sessions with tutors,
- programming exercices on the INGInious platform?
Evaluation methods
A programming assignment is due each week.
A mid-term evaluation will be organised halfway throughout the quadrimester.
The end-of-term exam aims to assess both the understanding of the course material and the capacity to apply it to write simple but correct Python programs.
The final course mark takes into account the mid-term evaluation and assignments during the quadrimester, in addition to the mark of the end-term exam.
The assignments and mid-term evaluation cannot be retaken for the June or September sessions.
If the mark for the mid-term evaluation is higher than that for the end-term exam, it will count for 1/3 and the mark of the end-term exam for 2/3.
If the mark for the mid-term evaluation is lower than that for the end-term exam, only the mark for the exam will be used to calculate the final course mark.
A bonus of 1 point will be granted to students who have participated in and regularly submitted their programming assignments during the quadrimester.
In case of plagiarism detection confirmed by a plagiarism detection tool the course teachers reserve the right to invite the student to pass an oral interrogation.
A mid-term evaluation will be organised halfway throughout the quadrimester.
The end-of-term exam aims to assess both the understanding of the course material and the capacity to apply it to write simple but correct Python programs.
The final course mark takes into account the mid-term evaluation and assignments during the quadrimester, in addition to the mark of the end-term exam.
The assignments and mid-term evaluation cannot be retaken for the June or September sessions.
If the mark for the mid-term evaluation is higher than that for the end-term exam, it will count for 1/3 and the mark of the end-term exam for 2/3.
If the mark for the mid-term evaluation is lower than that for the end-term exam, only the mark for the exam will be used to calculate the final course mark.
A bonus of 1 point will be granted to students who have participated in and regularly submitted their programming assignments during the quadrimester.
In case of plagiarism detection confirmed by a plagiarism detection tool the course teachers reserve the right to invite the student to pass an oral interrogation.
Online resources
All course material will be made available online: slides, syllabus, exercices, ...
Faculty or entity
INFO
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Minor in numerical technologies and society
Master [120] in Data Science : Statistic
Master [120] in Linguistics
Additionnal module in Geography
Bachelor in Mathematics
Bachelor in Computer Science
Approfondissement en statistique et sciences des données
Minor in Computer Sciences
Minor in Statistics, Actuarial Sciences and Data Sciences
Certificat d'université : Statistique et science des données (15/30 crédits)