Duration
10h Th, 20h APP
Number of credits
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
The theoretical part of this course will cover:
- Generalities on the nature and format of data in health sciences
- The different categories of data, their production and use
- Analysis methodologies and the concepts of reproducibility and automation
- Analysis of simple to complex data
- Use of existing databases
- Visual representation of data and results
- Biological interpretation of results
- Automation of analyses and the concept of "script"
Learning outcomes of the learning unit
At the end of this course, the student will be able to:
- understand the generalities of the different data in life sciences
- identify the resources available online
- identify the important points of a dataset when it is presented to him and carry out a relevant analysis
- represent the results in several ways to answer different questions
- interpret the biological value of the results
- respect the notions of reproducibility and automation by creating an analysis script that can be adapted to different data sets
Prerequisite knowledge and skills
This training is based on students' prior learning of the R programming language.
Planned learning activities and teaching methods
After learning the important concepts during the theoretical courses, the student will have to carry out data analyses with increasing complexity. He will understand how to carry out a particular analysis, then how to integrate the concepts of reproducibility and automation. For the practical work, the RStudio interface will be used to exploit the R programming environment.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Further information:
Lectures and practical work in face to face
Course materials and recommended or required readings
Platform(s) used for course materials:
- Microsoft Teams
Further information:
Different files will be made available to students:
- Theoretical and practical course slides
- Important resource documents
- Datasets
- Examples of scripts
Exam(s) in session
Any session
- In-person
oral exam
Further information:
The evaluation will include an analysis to be carried out by the student. He may have different resources (defined during the courses) at his disposal during the exam.
The evaluation criteria include:
- Carrying out the analysis in its entirety (30%)
- The mastery and understanding of the key steps of the analysis carried out (70%)
Work placement(s)
Organisational remarks and main changes to the course
Contacts
Arnaud LAVERGNE, PhD
GIGA-BIOINFORMATICS
B34 - 1st Floor
+32 4 3663453
arnaud.lavergne@uliege.be