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
Basic concepts of descriptive statistics are taught, interpreted and mathematically justified in this course. More precisely, here follows the content of the course: - Statistical tables and graphics - Summary statistics (central tendency, dispersion and shape) - Correlation analysis and linear regression
Basic concepts of descriptive statistics are taught, interpreted and mathematically justified in this course. More precisely, here follows the content of the course: - Statistical tables and graphics - Summary statistics (central tendency, dispersion 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 taught statistical software.
Moreover, the student should be able to outline the advantages and disadvantages of the different techniques. He/she should alos be aware of their limitaiton in practice, using their mathematicial properties.
After the course, the student should be able to present and interpret data in an adequate manner, in particular using the taught statistical software.
Moreover, the student should be able to outline the advantages and disadvantages of the different techniques. He/she should alos be aware of their limitaiton in practice, using their mathematicial properties.
Prerequisite knowledge and skills
Basic concepts of analysis and algebra, taught in secondary school.
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 on exercises and data analyses on the statistical software R (with goupr discussions) and personal work via some MCQ on line.
The learning activities are of three different types: theory lectures, practicals on exercises and data analyses on the statistical software R (with group discussions) and personal work via some MCQ on line.
Mode of delivery (face to face, distance learning, hybrid learning)
The courses and the tutorials are given face-to-face over the second semester according to a timetable available on Celcat.
The theory lectures are recorded by means of the podcast equipment installed in the lecture room. The students may visualize these recordings when they want.
Face-to-face course
Additional information:
The courses and the tutorials are given face-to-face over the second semester according to a timetable available on Celcat.
Videos of the theory lectures will be avaible through eCampus.
Recommended or required readings
Lecture notes and the slides used during the lectures are on line on e Campus.
Lecture notes and the slides used during the lectures are on line on e Campus.
Assessment methods and criteria
The final grade is obtained as follows:
- 5% for the mean result obtained by the student at the multiple choice questions on line during the semester
- 95% of the grade comes from the exam with the following parts: exercises and data analyses (50% of the global grade), theory and new MCQ (45%).
A minimal grade of 5/20 is required in the different parts of the exam in order to succeed in the course. Additional details on that constraint will be outlines during the course at the beginning and at the end of the semester.
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions )
Continuous assessment
Additional information:
The final grade is obtained as follows:
- 10% for the mean result obtained by the student at the multiple choice questions on line during the semester
- 90% of the grade comes from the exam with the following parts: exercises and data analyses (45% of the global grade), theory and new MCQ (45%).
A minimal grade of 5/20 is required in the different parts of the exam in order to succeed in the course. MCQ are compulsory : if two or more are missed, the MCQ grade will be set at 0. Additional details on that constraint will be outlines during the course at the beginning and at the end of the semester.
Work placement(s)
Organizational remarks
The organisation of the course is a bit different with resepct to last year. The student who repeat the course should pay attention to that.
Contacts
Professor: G. Haesbroeck
Assistant: S. Klenkenberg
Professeur: Arnout Van Messem
Assistant: Carole Baum