| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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No sequentiality
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| Degree programme | | Study hours | Credits | P2 SBU | P2 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 1st year Master Bioinformatics | Compulsory | 216 | 8,0 | 216 | 8,0 | Yes | No | Numerical |  |
| 1st year Master Bioinformatics - icp | Compulsory | 216 | 8,0 | 216 | 8,0 | Yes | No | Numerical | |
| 1st year Master Biostatistics | Compulsory | 216 | 8,0 | 216 | 8,0 | Yes | No | Numerical | |
| 1st year Master Biostatistics - icp | Compulsory | 216 | 8,0 | 216 | 8,0 | Yes | No | Numerical | |
| 1st year Master Data Science | Compulsory | 216 | 8,0 | 216 | 8,0 | Yes | No | Numerical | |
| 1st year Quantitative Epidemiology | Compulsory | 216 | 8,0 | 216 | 8,0 | Yes | No | Numerical | |
| 1st year Master Quantitative Epidemiology - icp | Compulsory | 216 | 8,0 | 216 | 8,0 | 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 design 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. | | | - 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. | | | - DC
| The student is an effective oral communicator in their own field. | | | - DC
| The student is an effective oral communicator, 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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Contents "Multivariate and Hierarchical Data":
- Repeated measures
- Clustered data
- Multivariate methods.
Contents "Discovering Associations":
- Sample size calculations
- Statistical research for pharmaceutical research and development
- Ethical aspects of consulting, reporting
- Statistical consulting training & protocol for the design of experiments
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Distance learning ✔
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Lecture ✔
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Project ✔
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Small group session ✔
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Period 2 Credits 8,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 40 % |
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| Oral evaluation during teaching period | 5 % |
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | All components have to be taken up.
Note: For the oral part of the exam, the range is [-4;+5]. This penalizes an imbalance between good reports and poor individual performance. |
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| Consequences | Students get a 0 score if they do not meet the condition. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | The assignment for the second chance exam remains the same. The structure of the oral exam for the second chance remains the same. |
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| Prerequisites |
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It is assumed that the student is familiar with: concepts of probability and statistics, linear models (regression, analysis of variance), logistic regression |
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| Compulsory course material |
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All course material is made available through BlackBoard, by the lecturers. |
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| Exchange Programme Statistics | Optional | 216 | 8,0 | 216 | 8,0 | Yes | Yes | Numerical | |
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Contents "Multivariate and Hierarchical Data":
- Repeated measures
- Clustered data
- Multivariate methods.
Contents "Discovering Associations":
- Sample size calculations
- Statistical research for pharmaceutical research and development
- Ethical aspects of consulting, reporting
- Statistical consulting training & protocol for the design of experiments
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|
|
|
|
|
|
|
|
Distance learning ✔
|
|
|
|
Lecture ✔
|
|
|
|
Project ✔
|
|
|
|
Small group session ✔
|
|
|
|
Period 2 Credits 8,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 40 % |
|
|
|
|
|
| Oral evaluation during teaching period | 5 % |
|
|
|
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | All components have to be taken up.
Note: For the oral part of the exam, the range is [-4;+5]. This penalizes an imbalance between good reports and poor individual performance. |
|
|
|
| Consequences | Students get a 0 score if they do not meet the condition. |
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | The assignment for the second chance exam remains the same. The structure of the oral exam for the second chance remains the same. |
|
|
|
|
|
| Prerequisites |
| |
It is assumed that the student is familiar with: concepts of probability and statistics, linear models (regression, analysis of variance), logistic regression |
|
|
| Compulsory course material |
| |
All course material is made available through BlackBoard, by the lecturers. |
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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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