| Language of instruction : English |
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Sequentiality
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No sequentiality
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Change the language, year or choose another item in the dropdown list if it is available.
| 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
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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
| EC2: The holder of the degree has in-depth knowledge and understanding of the concepts, methods, and (research) techniques of transportation sciences. He/she is able to apply the concepts, methods and (research) techniques in the field of transportation sciences adequately and autonomously. |
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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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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.16.9, section 2. |
3 Education, Examination and Legal Position Regulations art.15.1, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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