| Language of instruction : English |
| Exam contract: not possible |
|
Sequentiality
|
| |
|
Mandatory sequentiality bound on the level of programme components
|
| |
| |
| |
Following programme components must have been included in your study programme in a previous education period
|
| |
|
Generalized Linear Models (3563)
|
6.0 stptn |
| |
|
Linear Models (3560)
|
5.0 stptn |
| |
|
Medical and Molecular Biology (3564)
|
6.0 stptn |
| |
|
| Degree programme | | Study hours | Credits | P2 SBU | P2 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 2nd year Master Bioinformatics | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | No | Numerical |  |
| 2nd year Master Bioinformatics - icp | 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 routinely monitors his/her own learning process and adjusts and improves it accordingly. | - 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 can put research and consulting aspects of one or more statistical fields into practice. | - 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 |
|
|
The student has knowledge about (generalized) linear models and molecular biology.
|
|
|
|
The course focuses on the analysis of different types of sequencing experiments, such as methylation sequencing, mRNA sequencing, ChIP sequencing and ATAC sequencing. Every class consists out of a lecture and a hands-on session.
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Project ✔
|
|
|
|
Small group session ✔
|
|
|
|
Period 2 Credits 3,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 35 % |
|
|
|
|
| Oral evaluation during teaching period | 0 % |
|
|
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
|
|
| Compulsory course material |
| |
All compulsory material will be made available on Blackboard |
|
|
|
|
|
| Exchange Programme Statistics | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | 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 routinely monitors his/her own learning process and adjusts and improves it accordingly. | - 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 can put research and consulting aspects of one or more statistical fields into practice. | - 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 |
|
|
The student has knowledge about (generalized) linear models and molecular biology.
|
|
|
|
The course focuses on the analysis of different types of sequencing experiments, such as methylation sequencing, mRNA sequencing, ChIP sequencing and ATAC sequencing. Every class consists out of a lecture and a hands-on session.
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Project ✔
|
|
|
|
Small group session ✔
|
|
|
|
Period 2 Credits 3,00 Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
|
|
| Compulsory course material |
| |
All compulsory material will be made available on Blackboard |
|
|
|
|
|
1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.16.9, section 2. |
3 Education, Examination and Legal Position Regulations art.15.1, section 3.
|
| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
|