| 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 | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 1st year Master Bioinformatics | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical |  |
| 1st year Master Bioinformatics - icp | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 1st year Master Biostatistics | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 1st year Master Biostatistics - icp | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 1st year Master Data Science | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 1st year Quantitative Epidemiology | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 1st 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. | - EC
| The student is able to efficiently acquire, store and process data. | - EC
| The student can critically appraise methodology and challenge proposals for and reported results of data analysis. | - 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 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. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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Basic concepts of R, R studio and R markdown. Visualizing data using the lattice and ggplot 2 R packages. Basic programming in R, the tidyverse package and data wrangling
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Lecture ✔
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Project ✔
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Self-study assignment ✔
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Period 1 Credits 3,00
| Evaluation method | |
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| Written exam | 66 % |
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| Other | a a project (take home) + computer exam in class |
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| Exchange Programme Statistics | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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Basic concepts of R, R studio and R markdown. Visualizing data using the lattice and ggplot 2 R packages. Basic programming in R, the tidyverse package and data wrangling
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Lecture ✔
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Project ✔
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Self-study assignment ✔
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Period 1 Credits 3,00
| Evaluation method | |
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| Written exam | 66 % |
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| Other | a a project (take home) + computer exam in class |
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| 2nd year Master of Transportation Sciences | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| The holder of the degree has an advanced level of knowledge and understanding, typical of scientific work in the field of transportation sciences. | - EC
| The holder of the degree is able to autonomously carry out research in transportation sciences, formulate recommendations and show their practical applicability in daily life, whilst keeping to the deontological codes of research. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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|
Basic concepts of R, R studio and R markdown. Visualizing data using the lattice and ggplot 2 R packages. Basic programming in R, the tidyverse package and data wrangling
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Project ✔
|
|
|
|
Self-study assignment ✔
|
|
|
|
Period 1 Credits 3,00
| Evaluation method | |
|
| Written exam | 66 % |
|
|
|
| Other | a a project (take home) + computer exam in class |
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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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