15h Th, 10h Pr, 25h Proj.
Number of credits
|Bachelor in engineering||3 crédits|
|Bachelor in computer science||3 crédits|
|Master in data science (120 ECTS)||3 crédits|
|Master in data science and engineering (120 ECTS)||3 crédits|
Language(s) of instruction
Organisation and examination
Teaching in the first semester, review in January
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
- Link between probability and statistics
- Data generation and exploration
- Parametric estimation and confidence intervals
- Hypothesis testing
- Association methods
Learning outcomes of the learning unit
The student will understand the fundamental principles of statistics, and he will be able to apply them to carry out exploratory data analyses, population parameter estimation, and hypothesis testing. He will also understand the nature of regression and variance analysis problems, as well as the interest of non-parametric methods.
Prerequisite knowledge and skills
Calculus, algebra, geometry and probability. Elements of computer science and applied mathematics.
Planned learning activities and teaching methods
Theory is given via 6 lectures, in French. This is completed by exercise sessions (5) and by a practical project. The project will lead the student from exploratory data analysis towards parameter estimation and hypothesis testing, all this based on a real-life dataset of interest to the student. The project will be carried out by groups of two students, is mandatory, and its evaluation counts for 25% of the final grade.
Mode of delivery (face-to-face ; distance-learning)
Recommended or required readings
Students will have access to the slides used for the theory lectures and exercise notes.
Regarding the theory part, see http://www.montefiore.ulg.ac.be/~lwh/Stats/ Regarding the exercise and practical projects, see http://www.montefiore.ulg.ac.be/~lduchesne/stats
Assessment methods and criteria
The note is composed of two parts: the practical projects (25% of the final note) and a written exam. The latter is composed of three parts: the theory (25% of the final note), a question about the project (5%), and exercises (45% of the final note).
2013-2014 corresponds to the first instance of this new version of the course.
Professor: Louis WEHENKEL
Main Assistant: VAN LISHOUT François (L.Duchesne@ulg.ac.be)
Please contact us by email, using the following subject: "course: Elements of statistics". We will then fix an appointment to meet at Montefiore if necessary.