| Language of instruction : English |
| Exam contract: not possible |
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Prerequisites
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No sequentiality
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| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 2nd year Master Bioinformatics - icp | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical |  |
| 2nd year Master Biostatistics - icp | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 2nd year Master Quantitative Epidemiology - icp | Compulsory | 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 has the habit to assess data quality and integrity. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student knows the societal relevance of statistics and data science. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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Selected topics in computational biology are covered in this course each academic year.
For AY 2021-2022, the course will cover the following topics:
- Analysis of Gene Expression Data
- Biological Networks and clustering
- Inference of Gene Regulatory Networks using Differential Equations
- Hidden Markov Models and their Applications to Bioinformatics
- Computational Models for Gene prediction
This course is organised in cooperation with lecturers from our South partners.
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Lecture ✔
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Project ✔
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Paper ✔
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Presentation ✔
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Period 1 Credits 3,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 50 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Written exam | 50 % |
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| Multiple-choice questions | ✔ |
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| Use of study material during evaluation | ✔ |
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| Explanation (English) | The exam is divided into two parts. The first part will be closed book, followed by an open-book exam. |
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| Additional information | No participation at all in the homework will imply exclusion of participation in the final exam and incomplete participation will result in a reduced score of the final score. |
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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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Corequisite for this course is basic knowledge of fundamental statistical concepts. Although no specific pre-requisites, this course will cover advanced statistical topics. |
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| Compulsory course material |
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Handouts made available by the instructors on Blackboard. |
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| 2nd year Master Bioinformatics | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 2nd year Master Biostatistics | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 2nd year Master Quantitative Epidemiology | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 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 has the habit to assess data quality and integrity. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student knows the societal relevance of statistics and data science. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
Selected topics in computational biology are covered in this course each academic year.
For AY 2021-2022, the course will cover the following topics:
- Analysis of Gene Expression Data
- Biological Networks and clustering
- Inference of Gene Regulatory Networks using Differential Equations
- Hidden Markov Models and their Applications to Bioinformatics
- Computational Models for Gene prediction
This course is organised in cooperation with lecturers from our South partners.
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Project ✔
|
|
|
|
|
|
|
|
Paper ✔
|
|
|
|
Presentation ✔
|
|
|
|
Period 1 Credits 3,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 50 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
| Written exam | 50 % |
|
|
|
| Multiple-choice questions | ✔ |
|
|
|
|
|
| Use of study material during evaluation | ✔ |
|
| Explanation (English) | The exam is divided into two parts. The first part will be closed book, followed by an open-book exam. |
|
|
|
| Additional information | No participation at all in the homework will imply exclusion of participation in the final exam and incomplete participation will result in a reduced score of the final score. |
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
|
|
| Prerequisites |
| |
Corequisite for this course is basic knowledge of fundamental statistical concepts. Although no specific pre-requisites, this course will cover advanced statistical topics. |
|
|
| Compulsory course material |
| |
Handouts made available by the instructors 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.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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