2023-2024 / MATH7370-1

Descriptive statistics


16h Th, 8h Mon. WS, 8h Pr

Number of credits

 Bachelor of Science (BSc) in Computer Science3 crédits 


Arnout Van Messem

Language(s) of instruction

French language

Organisation and examination

Teaching in the second semester


Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

In this course, some basic concepts of descriptive statistics are introduced, interpreted and mathematically justified. More precisely, the content of the course is the following:

  • Basic concepts: types of variables, percentages and rates
  • Data presentation and visualization (through tables and graphs)
  • Summary statistics (through statistical parameters of location, scale and shape)
  • Correlation analysis and linear regression

Learning outcomes of the learning unit

After the course, the student should be able to present and interpret data in an adequate manner, in particular using the statistical software package R.

Moreover, the student should be able to outline the advantages and disadvantages of the different techniques. He/she should also be aware of their limitations for practical use (based on their mathematicial properties).



Prerequisite knowledge and skills

Basic concepts of analysis and algebra, taught in secondary school.

Planned learning activities and teaching methods

The learning activities are of three different types:

  • theory lectures,
  • practicals: written exercises and data analyses using the statistical software R (with possible group discussions) and
  • personal work via online multiple choice questions.


Mode of delivery (face to face, distance learning, hybrid learning)

Face-to-face course

Recommended or required readings

The lecture notes, the slides used during the theory sessions, and the exercices will be made available through eCampus. 


Exam(s) in session

Any session

- In-person

written exam ( multiple-choice questionnaire, open-ended questions )

Continuous assessment

Additional information:

The final grade is obtained as follows:

  • 10% corresponds to the mean result of the online multiple choice questions during the semester;
  • 45% corresponds to the result of a written examination (theory, MCQ, and exercises);
  • 45% corresponds to the result of an examination project (exercises and data analyses using the statistical software package R).
  • 45% corresponds to the result of a practical examination (exercises and data analyses using the statistical software package R).
A minimal grade of 7/20 is required for each of the different parts of the examination (written examination and project) in order to succeed the course. If at least one of these grades is below 7/20, the global grade will be limited to 7/20.

Participation in the MCQ is compulsory: if the student does not take part in two or more MCQ, the MCQ grade will be set to 0.



Work placement(s)

Organisational remarks and main changes to the course


Professeur: Arnout Van Messem
Assistant: Carole Baum

Association of one or more MOOCs