Introduction to programming

linfo1101  2021-2022  Louvain-la-Neuve

Introduction to programming
5.00 credits
30.0 h + 30.0 h
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 :

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.
  • 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?
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
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.
Online resources
All course material will be made available online: slides, syllabus, exercices, ...
Faculty or entity

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Learning outcomes
Master [120] in Linguistics

Additionnal module in Geography

Minor in Computer Sciences

Minor in numerical technologies and society

Bachelor in Computer Science

Bachelor in Mathematics

Approfondissement en statistique et sciences des données

Minor in Statistics, Actuarial Sciences and Data Sciences

Certificat d'université : Statistique et sciences des données (15/30 crédits)

Master [120] in Data Science : Statistic