linfo1101  2020-2021  Louvain-la-Neuve

Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 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.
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:
  • S1.I2
  • S2.2, S2.4
Students who have successfully completed this course will be able to :
  • Apply the concepts, laws, reasonings to a disciplinary problem of squared complexity.
  • Describe adequate modeling and calculation tools to solve a disciplined disciplinary problem.
  • Model a problem and design one or more technical solutions that meet the specifications
  • Implement and test a solution in the form of a model, a prototype and / or a digital model.
  • Commit collectively to a work plan, a timetable (and roles to keep).
  • Communicate in graphical and schematic form interpret a diagram, present the results of a work, structure information.
  • Read, analyze and exploit technical documents (standards, plans, specifications, specifications, ...).
  • Write written summary documents taking into account the requirements of the missions (projects and problems).
  • Demonstrate a good understanding of the concepts and methodology of object-oriented programming.
  • Make good use of the elements of an object-oriented language such as Python.
 
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

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

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?
Even though preference will be given to face-to-face teaching sessions, depending on the health situation and the number of students enrolled, other forms of teaching and evaluation (online, co-modal or hybrid) may be considered.
Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

A programming assignment is due each week.  A mid-term evaluation takes place in the middle of the first semester.  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 grade can take into account the mid-term evaluation and the work done during the quadrimester, in addition to the grade from the exam.  The assignments and the mid-term evaluation cannot be retaken for the June or September sessions.

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
Aims
Master [120] in Data Science : Statistic
DATS2M
5
Master [120] in Linguistics
LING2M
5
Approfondissement en sciences et technologies de l'information et de la communication (pour seule réinscription)
APPSTIC
5
Minor in Computer Sciences
MINSINF
5
Certificat d'université : Statistique et sciences des données (15/30 crédits)
STAT2FC
5
Minor in Statistics, Actuarial Sciences and Data Sciences
MINSTAT
5
Bachelor in Computer Science
SINF1BA
5
Bachelor in Mathematics
MATH1BA
6
Additionnal module in Geography
APPGEOG
5
Approfondissement en statistique et sciences des données
APPSTAT
5
Minor in numerical technologies and society
MINSTIC
5