Duration
25h Th, 15h Pr, 10h Mon. WS
Number of credits
| Bachelor in mathematics | 5 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, the classical methods of inferential statistics will be reviewed and further developped, with the theory of hypothesis testing detailed. Then, an introduction to some techniques used in multivariate statistics will be presented (i.e. Principal Component Analysis and clustering).
Learning outcomes of the learning unit
After this course, the students should be able to take decisions based on the result of statistical testing. Moreover, they should have acquired the basic approach of high dimensional data analysis.
Prerequisite knowledge and skills
Probability and some elements of inferential statistics (estimation).
Planned learning activities and teaching methods
The course consists of ex-cathedra lectures on the theory and there are alors 15 hours of practicals (exercises and data analyses on statistical softwares). Personal work (for ex: first manipulaitons of the softwares) completes the learning.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Additional information:
The courses and the tutorials/practicals are given face-to-face over the second semester according to a timetable distributed to the students in the beginning of the academic year.
Recommended or required readings
(Partial) Course notes, slides and exercises sheets will be made available along the year on eCampus.
Assessment methods and criteria
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions )
Additional information:
The final mark is a weighted mean of the marks attributed to the two following assesments:
- written exam on theory and exercises (without access to personnal notes nor usage of software)
- written exam in the computer room for a data analysis
A grade inferior to 6/20 in any of the parts will automatically lead to a failed mark for the course.
Work placement(s)
Organizational remarks
Contacts
G. Haesbroeck (G.Haesbroeck@uliege.be)
S. Klenkenberg (S.Klenkenberg@uliege.be)