| Language of instruction : English |
| Exam contract: not possible |
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Prerequisites
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Mandatory sequentiality bound on the level of course units
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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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Concepts of Epidemiology DL (3585)
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4,0 stptn |
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Introduction to Bayesian Inference DL (3579)
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4,0 stptn |
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| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | second year Quantitative Epidemiology - distance learning | Compulsory | 108 | 4,0 | 108 | 4,0 | 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 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. | | | - DC
| The student is an effective writer in their own field. | | | - DC
| The student is an effective writer, both within their own field as well as across disciplines. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The geographical representation of the occurrence of a disease and investigation of the relationship between risk of disease and environmental factors are important topics in the analysis of public health. The objective of this course is to give an introduction to the theory and practice of spatial data analysis in the context of disease mapping and point pattern analysis. This course will focus on methods for spatial data handling, Bayesian hierarchical models, and point pattern simulation. The student acquires knowledgde about spatial data modeling. The student can apply these to real data problems, using R and OpenBugs.
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Distance learning ✔
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Project ✔
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Q&A session ✔
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Period 1 Credits 4,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 30 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Use of study material during evaluation | ✔ |
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| Explanation (English) | Slides, course notes and project |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | Score for project is carried over to the retake exam. |
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| Prerequisites |
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A prerequisit for this course is the succesful completion of the cource "Concepts in Bayesian Inference". |
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| Compulsory course material |
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All course material (slides, code, papers) will be made available via Blackboard |
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| second year Master Bioinformatics - distance learning | Optional | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
| second year Master Biostatistics - distance learning | Optional | 108 | 4,0 | 108 | 4,0 | 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 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. | | | - DC
| The student is an effective writer in their own field. | | | - DC
| The student is an effective writer, both within their own field as well as across disciplines. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
The geographical representation of the occurrence of a disease and investigation of the relationship between risk of disease and environmental factors are important topics in the analysis of public health. The objective of this course is to give an introduction to the theory and practice of spatial data analysis in the context of disease mapping and point pattern analysis. This course will focus on methods for spatial data handling, Bayesian hierarchical models, and point pattern simulation. The student acquires knowledgde about spatial data modeling. The student can apply these to real data problems, using R and OpenBugs.
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|
|
|
|
|
|
|
|
Distance learning ✔
|
|
|
|
Project ✔
|
|
|
|
Q&A session ✔
|
|
|
|
Period 1 Credits 4,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 30 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
|
| Use of study material during evaluation | ✔ |
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| Explanation (English) | Slides, course notes and project |
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|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | Score for project is carried over to the retake exam. |
|
|
|
|
|
| Prerequisites |
| |
A prerequisit for this course is the succesful completion of the cource "Concepts in Bayesian Inference". |
|
|
| Compulsory course material |
| |
All course material (slides, code, papers) will be made available via 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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