| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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Mandatory sequentiality bound on the level of programme components
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For following programme components you must have acquired a credit certificate, exemption, already tolerated unsatisfactory grade or selected tolerable unsatisfactory grade.
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Generalized Linear Models (3563)
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6,0 stptn |
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Linear Models (3560)
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8,0 stptn |
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Medical and Molecular Biology (3564)
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6,0 stptn |
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| 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 | |
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| | | 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. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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- 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
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Lecture ✔
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Project ✔
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Small group session ✔
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Period 2 Credits 3,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 35 % |
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| Oral evaluation during teaching period | 0 % |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Prerequisites |
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Knowledge about (generalized) linear models, and molecular biology is required |
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| Compulsory course material |
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All compulsory material will be made available on Blackboard |
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| Exchange Programme Statistics | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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| | | 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 |
|
- 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
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Lecture ✔
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Project ✔
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Small group session ✔
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Period 2 Credits 3,00 Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Prerequisites |
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Knowledge about (generalized) linear models, and molecular biology is required |
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| Compulsory course material |
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All compulsory material will be made available on Blackboard |
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1 examination regulations art.1.3, section 4. |
| 2 examination regulations art.4.7, section 2. |
3 examination regulations art.2.2, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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