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2026-2027 / ENVT4124-1

Environmental data processing

Duration

10h Th, 10h Pr, 10h Mon. WS

Number of credits

 Master in environmental sciences and management, professional focus3 crédits 

Lecturer

Laurent Loosveldt

Language(s) of instruction

French language

Organisation and examination

Teaching in the second semester

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

The course deepens the statistical knowledge needed by science students.

The course begins with a review of the concept of statistical hypothesis testing. This is illustrated through tests concerning the mean of a normal distribution and tests concerning a proportion. Then, the course focuses on bivariate statistics with particular interest for three essential pratical situiations:

  • case of a qualitative explanatory variable and a continuous response variable
  • case of continous explanatory and response variables
  • case of qualitative explanatory and response variables
Along the way, the course presents tools from bivariate descriptive statistics as well as various foundamental approches from bivariate statistical inference such as linear regression and hypotheses tests that compare 2 or more parameters (2-sample t-test, F-test, ANOVA test, chi-squared test). 

The course focuses on the understanding of the statistical process, the ciritical interpretation of statistical results, and the application of the studied statistical methods, by making use of the statistical software R. The student will be required to use R through the RStudio interface. They will perform statistical processing using lines of code, while simultaneously being introduced to fundamental concepts of statistics.on and its implementation using RStudio

 

Learning outcomes of the learning unit

The course aims at providing the students with the necessary tools to

  • Understand the notion of hypothesis testing in statistics
  • Understand and use bivariate statistical tools and linear regression.
  • Understand statistical results in their context. Be able to read, in a critical way, numerical or graphical statistical results (linked to the methods seen in the course).
  • Understand the challenges, benefits and limitations of statistical studies.
  • Having the necessary vocabulary/background to be able to interact with a statistician in the context of an environmental problem.
  • Be able to use the statistical software R and interpret its output.
  • To develop a critical perspective on statistical methods and tools.
  • Use the open-source program R through the RStudio interface by writing code
  • Import a file into their workspace
  • Manipulate and explore a database
  • Perform basic statistical analyses
  • Carry out several statistical tests via the RStudio interface and interpret the results
  • Perform a linear regression using RStudio
  • Search online for information related to coding

Prerequisite knowledge and skills

Notions of mathematics.

Basics of statistics: probability, descriptive (univariate) statistics, confidence intervals, hypothesis testing.

In general terms:

  • Knowledge of how to use a computer
  • Basic knowledge of Excel
  • Basic knowledge of statistics (descriptive statistics)
If the student attends the course with his/her personal computer, he must:

  • Configure the keyboard correctly according to the keys (AZERTY or QUERTY)
  • Know how to install a computer program on his/her computer

Planned learning activities and teaching methods

Lectures, exercices sessions (written and computer-based) and learning through online videos and practice exercises

In addition to the lectures and exercices sessions, teaching activities will be delivered through videos on the e-campus platform. In these videos, the theoretical concepts presented will be directly applied using the R software. Students will then be invited to test their understanding of these concepts and practice the R commands introduced through quizzes on e-campus.

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

Blended learning


Further information:

Face-to-face classes are delivered on Arlon campus.

In addition, videos are available on e-campus and are accompanied by online formative exercises.

Course materials and recommended or required readings

Platform(s) used for course materials:
- eCampus


Further information:

The course slides, practical session materials, and videos to be watched are made available on eCampus.

Regarding basic Excel knowledge, we refer, for example, to the following site:

https://openclassrooms.com/fr/courses/7168336-maitrisez-les-fondamentaux-dexcel

Exam(s) in session

Any session

- In-person

written exam ( open-ended questions )


Further information:

The assessment focuses on the correct use and understanding of statistical techniques, the interpretation of results, as well as the use of the R statistical software.

The exam will consist of a critical analysis of a portfolio of R software outputs, which should be used to produce a statistical study of a given problem.

Any attempt at cheating will result in a zero grade. In particular, the use of a mobile phone or AI software is strictly prohibited throughout the exam. Any attempt to communicate with other students will also result in a zero grade.




 

Work placement(s)

Organisational remarks and main changes to the course

See the notes available in the course ENVT0048-2 to freshen up the basics of statistics. These notes will be made available through eCampus.

The student is requested to inform the teacher if he/she does not have a personal computer.

Contacts

Laurent Loosveldt

Institut de Mathématique - B37 - Bureau 0/59

Quartier Polytech 1

Allée de la découverte, 12

4000 Liège (Sart-Tilman)

Tél. : (04) 366.92.56.

E-mail : l.loosveldt@uliege.be

 

Association of one or more MOOCs