Duration
10h Th, 20h APP
Number of credits
| Master in biomedicine, research focus | 3 crédits | |||
| Master in biomedicine, professional focus in biomedical data management | 3 crédits | |||
| Master in biomedicine, professional focus in quality assurance | 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
The theoretical part of this course will cover:
- General concepts regarding the nature and format of data in health sciences
- The principles of reproducibility, automation, and reporting
- The Tidyverse concept
- The use and benefits of R Markdown
- Learning basic R commands - Tidyverse
- Learning about data types - Tidyverse
- Data table manipulation - Tidyverse
- Visualizing data and results - ggplot2
- Automating analyses and understanding the concept of scripts
- Reporting - RMarkdown
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
- 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
January exam session
- In-person
written exam AND oral exam
Written work / report
Further information:
The assessment will include:
- A written examination covering the theoretical concepts taught in the course.
- An analysis script to be prepared by the student and submitted for the oral examination. During the oral examination, the student will be provided with a dataset to test their work. Students will be allowed to use various resources during the examination, as specified during the course.
The assessment will be weighted as follows:
- Participation in practical sessions: 15%
- Written report: 25%
- Written examination: 20%
- Oral examination: 40%
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