| 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 | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical |  |
| 1st year Master Bioinformatics - icp | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
| 1st year Master Biostatistics | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
| 1st year Master Biostatistics - icp | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
| 1st year Master Data Science | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
| 1st year Quantitative Epidemiology | Compulsory | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
| 1st year Master Quantitative Epidemiology - icp | Compulsory | 135 | 5,0 | 135 | 5,0 | 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 routinely monitors his/her own learning process and adjusts and improves it accordingly. | - 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 knows the societal relevance of statistics and data science. | | | - DC
| The student can reflect on and explain the societal relevance of a task, particularly within the programme specialization | - EC
| The student knows the ethical, moral, legal, policy making, and privacy context of statistics and data science, and always acts accordingly. | | | - DC
| The student can explain basic principles regarding ethics and integrity in general. | | | - DC
| The student can apply basic principles regarding ethics and integrity to the fields of statistics and data science. | | | - DC
| The student acts according to societal and ethical standards in general and particularly within the fields of statistics and data science. | - 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. | - EC
| The student knows the relevant stakeholders and understands the need for assertive and empathic interaction with them. | | | - DC
| The student can identify relevant stakeholders and their interests, particularly within the programme specialization. | | | - DC
| The student can reflect on the role of the statistician and data scientist in the interaction with the stakeholders. | | | - DC
| The student can, when building an argumentation, consider different perspectives and interests. | | | - DC
| The student can explain the consequences of his/her work for relevant stakeholders. | - 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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This course aims to integrate knowledge and skills acquired in other courses (Concepts of Probability and Statistics, Data Management, Programming in R and Linear Models). It takes the form of a group project assignment. No regular lectures are given, but rather a few seminars are organised. No new theory is provided by the seminars, but rather skills that are helpful for bringing the project assignment to a good end. Apart from data management and data analysis skills, the course also focuses on collaborative skills, reporting, ethical and societal aspects, and scientific integrity.
This course is organised in the last two weeks of the 1st semester.
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Collective feedback moment ✔
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Lecture ✔
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Project ✔
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Q&A ✔
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Discussion/debate ✔
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Group work ✔
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Paper ✔
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Porfolio ✔
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Presentation ✔
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Period 1 Credits 5,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 70 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Conditions transfer of partial marks within the academic year | Participation in the group work. |
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| Other evaluation method during teaching period | 5 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Conditions transfer of partial marks within the academic year | Participation in the group work. |
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| Use of study material during evaluation | ✔ |
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| Explanation (English) | The student may use all course materials and her/his report, presentation and notes. |
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | The student must pass for (1) the total of the paper and the self-reflection assignment, (2) the oral exam, and (3) for the peer assessment (i.e. sufficient active participation in the group work). |
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| Consequences | If the student fails for one of these two components, the final mark will by the minimum of
- 9
- the total score of all evaluation components. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | If the student received a pass mark for the total of the paper and the self-reflection assignment and received a pass mark for the peer assessment, then the student only needs to redo the oral exam. The scores of the other aspects will be carried over to the second chance exam.
If the student did not receive a pass mark for the total of the paper and the self-reflection assignment, the student will get a new project assignment that she/he needs to do individually. The student will also have to redo the oral exam. The score for the peer-assessment will be carried over to the second chance exam. |
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| Prerequisites |
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There are no specific prerequisites for this course. Corequisite of this course are: Concepts of Probability and Statistics, Linear Models, and Programming in R. |
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| Compulsory course material |
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All course materialls will be available on Blackboard. |
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| Recommended course material |
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Course material related to the courses Concepts of Probability and Statistics, Linear Models, and Programming in R. |
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| Exchange Programme Statistics | Optional | 135 | 5,0 | 135 | 5,0 | Yes | Yes | Numerical | |
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| |
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|
This course aims to integrate knowledge and skills acquired in other courses (Concepts of Probability and Statistics, Data Management, Programming in R and Linear Models). It takes the form of a group project assignment. No regular lectures are given, but rather a few seminars are organised. No new theory is provided by the seminars, but rather skills that are helpful for bringing the project assignment to a good end. Apart from data management and data analysis skills, the course also focuses on collaborative skills, reporting, ethical and societal aspects, and scientific integrity.
This course is organised in the last two weeks of the 1st semester.
|
|
|
|
|
|
|
|
|
Collective feedback moment ✔
|
|
|
|
Lecture ✔
|
|
|
|
Project ✔
|
|
|
|
Q&A ✔
|
|
|
|
|
|
|
|
Discussion/debate ✔
|
|
|
|
Group work ✔
|
|
|
|
Paper ✔
|
|
|
|
Porfolio ✔
|
|
|
|
Presentation ✔
|
|
|
|
Period 1 Credits 5,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 50 % |
|
| Transfer of partial marks within the academic year | ✔ |
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|
|
|
|
|
|
| Other evaluation method during teaching period | 10 % |
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|
| Transfer of partial marks within the academic year | ✔ |
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|
|
|
|
|
| Use of study material during evaluation | ✔ |
|
| Explanation (English) | The student may use all course materials and her/his report, presentation and notes. |
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | The student must pass for (1) the total of the paper, the self-reflection assignment and the peer assessment, and for (2) the oral exam. |
|
|
|
| Consequences | If the student fails for one of these two components, the final mark will by the minimum of
- 9
- the total score of all evaluation components. |
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | If the student received a pass mark for the total of the paper and the self-reflection assignment, the student only needs to redo the oral exam. The score of the peer-assessment will also be carried over to the second chance exam.
If the student did not receive a pass mark for the total of the paper and the self-reflection assignment, the student will get a new project assignment that she/he needs to do individually. The student will also have to redo the oral exam. The score for the peer-assessment will be carried over to the second chance exam. |
|
|
|
|
|
| Prerequisites |
| |
There are no specific prerequisites for this course. Corequisite of this course are: Concepts of Probability and Statistics, Linear Models, and Programming in R. |
|
|
| Compulsory course material |
| |
All course materialls will be available on Blackboard. |
|
|
| Recommended course material |
| |
Course material related to the courses Concepts of Probability and Statistics, Linear Models, and Programming in R. |
|
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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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