2023-2024 / MATH0487-2

Elements of statistics


15h Th, 10h Pr, 25h Proj.

Number of credits

 Bachelor of Science (BSc) in Engineering3 crédits 
 Bachelor of Science (BSc) in Computer Science3 crédits 


Pierre Sacré

Language(s) of instruction

French language

Organisation and examination

Teaching in the first semester, review in January


Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

The course provides an introduction to the mathematical theory behind statistical methods and theoretical guarantees for the statistical methods that you may use for certain applications of engineering and science.

The following topics are addressed:
- Models, likelihood, and estimation;
- Point estimation (maximum likelihood, method of moments);
- Confidence interval;
- Regression;
- Hypothesis testing;
- Bayesian statistical inference.

Learning outcomes of the learning unit

At the end of the course, 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.

Prerequisite knowledge and skills

Calculus, algebra, geometry and probability. Elements of computer science and applied mathematics.

Planned learning activities and teaching methods

The course is composed of about 12 hours of theoretical lectures, 10 hours of classroom exercises, and 25 hours of assistance to the realization of practical projects with the computer.

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

Face-to-face course

Recommended or required readings

The course material will be made available as the semester progresses.

Additional references:
- Wasserman, Larry. All of Statistics: A Concise Course in Statistical Inference. Corrected second printing, 2005. Springer Texts in Statistics. New York, NY: Springer, 2010.
- Rice, John A. Mathematical Statistics and Data Analysis. New Delhi: Cengage Learning/Brooks/Cole, 2014.
- Riley, K. F., M. P. Hobson, and S. J. Bence. Mathematical Methods for Physics and Engineering. 3rd ed. Cambridge?; New York: Cambridge University Press, 2006.

Exam(s) in session

Any session

- In-person

written exam

Written work / report

Additional information:

The assessment is composed of two grades: a grade for the projects (approximately 15% of the final grade) and a grade for the written exam covering theory and exercises (approximately 85% of the final grade).

The projects will be subject to a single evaluation during the year and the grade obtained during the year will be used in determining the average grade for the first session and the second session (if applicable).

Work placement(s)

Organisational remarks and main changes to the course


Lecturer: Pierre Sacré (p.sacre@uliege.be). Webpage: https://people.montefiore.uliege.be/sacre/MATH0487/.

Association of one or more MOOCs