4 ECTS credits
105 h study time

Offer 1 with catalog number 4021572ENR for all students in the 2nd semester at a (E) Master - advanced level.

Semester
2nd semester
Enrollment based on exam contract
Impossible
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Yes
Taught in
English
Faculty
Faculty of Sciences and Bioengineering Sciences
Department
Bio-Engineering Sciences
Educational team
Sophie De Buyl (course titular)
Activities and contact hours

13 contact hours Lecture
26 contact hours Seminar, Exercises or Practicals
26 contact hours Independent or External Form of Study
Course Content
  • Covariance analysis
  • Dimensional reduction techniques (PCA, tSNE)
  • k-means, Hierarchical clustering, heat maps
  • Linear discriminant analysis and Neural networks for classification
  • ROC curves
  • Resampling methods
  • Big data processing: which analysis to choose when

Programming will be performed in Python.

Additional info

None

Learning Outcomes

General competences

The student:
- The student is capable of applying linear algebra in variance, covariance and correlation structures and understand geometrical equivalents of basic multivariate reasoning
- The student is capable of representing real data from large datasets in a comprehensible manner
- The student is capable of carrying out inference about multivariate means
- The student understands and applies basic ordination, discrimination and classification methodologies: Principal Components Analysis, Discriminant Analysis and Cluster Analysis
- The student makes use of existing software packages in R to analyse data.

Grading

The final grade is composed based on the following categories:
Other Exam determines 100% of the final mark.

Within the Other Exam category, the following assignments need to be completed:

  • Other exam with a relative weight of 1 which comprises 100% of the final mark.

Additional info regarding evaluation

Students will be evaluated on the basis of a project consisting in analysing a 'big' data set with methods seen in the course (50% of the final grade for the written report and 50% for an individual oral exam consisting in presenting the project as a basis to ask questions about the course content). 

Allowed unsatisfactory mark
The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context

This offer is part of the following study plans:
Master of Bioengineering Sciences: Cell and Gene Biotechnology: Medical Biotechnology (only offered in Dutch)
Master of Bioengineering Sciences: Cell and Gene Biotechnology: Molecular Biotechnology (only offered in Dutch)
Master of Bioengineering Sciences: Cell and Gene Biotechnology: Agrobiotechnology (only offered in Dutch)
Master of Bioengineering Sciences: Chemistry and Bioprocess Technology: Food Biotechnology (only offered in Dutch)
Master of Bioengineering Sciences: Chemistry and Bioprocess Technology: Chemical Biotechnology (only offered in Dutch)
Master of Bioengineering Sciences: Chemistry and Bioprocess Technology: Biochemical Biotechnology (only offered in Dutch)
Master of Biology: Molecular and Cellular Life sciences