| Language of instruction : English |
| Exam contract: not possible |
|
Sequentiality
|
| |
|
Mandatory sequentiality bound on the level of programme components
|
| |
| |
| |
For following programme components you must have acquired a credit certificate, exemption, already tolerated unsatisfactory grade or selected tolerable unsatisfactory grade.
|
| |
|
Generalized Linear Models DL (3580)
|
6,0 stptn |
| |
|
Linear Models DL (3577)
|
8,0 stptn |
| |
|
Medical and Molecular Biology DL (3581)
|
6,0 stptn |
| |
|
| Degree programme | | Study hours | Credits | P2 SBU | P2 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | second year Master Bioinformatics - distance learning | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | No | Numerical |  |
|
| | | Learning outcomes |
- EC
| The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software. | | | - DC
| ... correctly using state-of-the-art analysis methodology. | | | - DC
| ... correctly using state-of-the-art software. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student can critically appraise methodology and challenge proposals for and reported results of data analysis. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. | - EC
| The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application. | | | - DC
| The student is able to correctly use the theory methodologically, thus contributing to scientific research within the field of application. | | | - DC
| The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of application. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
- analysis of sequencing experiments, such as methylation sequencing, mRNA sequencing, single cell sequencing, et cetera
- generalized linear and additive models and their application within bio-informatics
- count regression; multiplicity
- high-throughput technologies within genomics
|
|
|
|
|
|
|
|
|
Distance learning ✔
|
|
|
|
Practical ✔
|
|
|
|
Project ✔
|
|
|
|
Period 2 Credits 3,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 35 % |
|
|
|
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
|
|
| Prerequisites |
| |
Knowledge of (generalized) linear models, and molecular biology are required |
|
|
| Recommended course material |
| |
All compulsory material will be made available on Blackboard |
|
|
|
|
|
1 examination regulations art.1.3, section 4. |
| 2 examination regulations art.4.7, section 2. |
3 examination regulations art.2.2, section 3.
|
| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
|