Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h + 30.0 h
Q1
Teacher(s)
Mens Kim; Nijssen Siegfried; Pecheur Charles;
Language
French
Main themes
- Introduction to programming;
- The Python programming language;
- Analysis of a computer science problem, design, specification and implementation of a solution;
- Linear data structures;
- Fundamental concepts of object-oriented programming.
Aims
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:
|
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
- Programs, source code and program execution
- Identifiers, variables, values, types, assignment
- Expressions, instructions
- 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, visibility
- Class composition, inheritance and encapsulation
- Polymorphism, super calls and dynamic binding
- Object equality
- Linked data structures
Teaching methods
The teaching methods used will encourage active student learning, through a mixture of :
- lectures,
- partical exercice sessions with tutors,
- programming exercices on the INGInious platform.
Evaluation methods
A mid-term evaluation will take place in the middle of the first semester. The score obtained for this exam will count for 1/3 of the final grade, but only if it is greater than the examination mark.
The end-term exam aims to assess both the understanding of the course material and the capacity to apply it to correctly write simple Python programs.
Online resources
All course material: slides, syllabus and exercices will be made available online.
Faculty or entity
INFO
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Approfondissement en sciences et technologies de l'information et de la communication (pour seule réinscription)
Approfondissement en statistique et sciences des données
Additionnal module in Geography
Minor in Statistics, Actuarial Sciences and Data Sciences
Minor in Computer Sciences
Minor in Information and Communication Studies and Technologies
Master [120] in Data Science : Statistic
Bachelor in Mathematics
Master [120] in Linguistics
Certificat d'université : Statistique et sciences des données (15/30 crédits)
Bachelor in Computer Science