Duration
16h Th, 8h Mon. WS, 8h Pr
Number of credits
| Bachelor of Science (BSc) in Computer Science | 3 crédits |
Lecturer
Language(s) of instruction
French language
Organisation and examination
Teaching in the second semester
Schedule
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 representation 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 represent 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
There are three different types of learning activities:
- theory lectures,
- written exercises and data analysis 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
Course materials and 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
Further information:
The final grade (both on the exam and the resit exam) is obtained as follows:
- 10% corresponds to the average 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 a practical examination (exercises and data analysis using the statistical software package R).
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. The score on the MCQ will be transferred to the resit examination. This part cannot be retaken.
Work placement(s)
Organisational remarks and main changes to the course
Contacts
Professeur: Arnout Van Messem
Assistant: Pauline Hrebenar