Duration
15h Th, 10h Pr
Number of credits
| Master in chemistry (120 ECTS) | 3 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This class aims to initiate students to statistical methods used in analytical chemistry. The class focuses on specific optimization, modelization, visualization, and data reduction methods. The class will cover theoretical and practical aspects for each of the method through a flip class format and project-based learning. Students will be invited to read about the different methods before applying them in class on the MetaboAnalyst platform. This platform is a first learning step to master the data processing tools and R language.
The class is given in English.
Learning outcomes of the learning unit
At the end of the class, the students will be able to: * Explain the concept behind the different methods * Explain the different analysis steps involved in a data processing workflow * Establish a workflow based on a data set * Explain the different steps for data pre-processing * Process data using uni- and multivariate methods in the MetaboAnalyst platform * Communicate the method used and the results obtained
Prerequisite knowledge and skills
The students have to master the basic concepts of analytical chemistry and statistic.
Planned learning activities and teaching methods
The class content is split into 5 learning modules.
1. Introduction et description of the class format
2. Establishment of the project with the teaching team
3. Research and discussion on the different techniques (DOE, pre-processing, visualization, data reduction, machine learning...).
4. Supervised assignment on MetaboAnalyst
5. Writing of the final report (this report can be associated with the realization of a thesis or dissertation)
Mode of delivery (face to face, distance learning, hybrid learning)
Due to the active and applied format, this class is a sit-in module.
In case of any restrictions due to COVID-19, the class will be thaught remotely.
Recommended or required readings
The class materials will be shared with the student prior to the first module.
Depending on the topic, recommended reading will be shared with the students.
The assessment will be based on a projet report using data generated by the student or provided by the teacher.
The assessment included the scientific content (statistical methods) and the communication efficiency.
COVID-19 special modalities
Based on the evaluation format, the assessment method will not be impacted by the COVID situation (color code).
Work placement(s)
NA.
Organisational remarks and main changes to the course
The class is organized around different modules. Each module lasts a half- day, depending on the students and teachers' availability.
The students are encouraged to contact the class teachers in the beginning of the term to establish the planning.
Contacts
Pierre-Hugues Stefanuto: phstefanuto@uliege.be