| Language of instruction: English |
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Mandatory sequentiality bound on the level of programme components
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Following programme components must have been included in your study programme in a previous education period
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Concepts of Probability and Statistics (1798)
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5.0 stptn |
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Linear Models (3560)
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5.0 stptn |
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Change the language, year or choose another item in the dropdown list if it is available.
There is no data for this choice.
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 | |
 | 2nd year Master Biostatistics | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical |  |
| 2nd year Master Biostatistics - icp | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 2nd year Master Data Science | 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. | | | - DC
| ... correctly using state-of-the-art analysis methodology. | | | - DC
| ... correctly using state-of-the-art design methodology. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student routinely monitors his/her own learning process and adjusts and improves it accordingly. | - 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 knows the international nature of the field of statistical science and data science. | - 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 explain ethical issues and dilemmas 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. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The student knows the basics of statistical inference and probability and linear models.
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In this course students will learn about some more advanced and state-of-the art statistical inference issues and techniques that are relevant for modern applications that go beyond the scope of traditional statistical methods :
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prediction versus association
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observational versus experimental studies
-
basics of causal inference and causal machine learning.
Examples (with R code) will also be discussed.
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Lecture ✔
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Project ✔
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Group work ✔
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Paper ✔
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Semester 1 (3,00sp)
| Evaluation method | |
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| Written evaluation during teaching period | 50 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Written exam | 30 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Oral exam | 20 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Other | Discussion about the paper. |
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| Use of study material during evaluation | ✔ |
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| Explanation (English) | All course materials and own notations may be used. |
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | To get a pass mark, the student must pass for each of the following parts: (project, paper and oral exam). |
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| Consequences | If the condition is not met, 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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| Compulsory course material |
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Course notes or slides will be made available on Blackboard. |
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| 2nd year Master Bioinformatics | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 2nd year Master Quantitative Epidemiology | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| Exchange Programme Statistics | Optional | 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. | | | - DC
| ... correctly using state-of-the-art analysis methodology. | | | - DC
| ... correctly using state-of-the-art design methodology. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student routinely monitors his/her own learning process and adjusts and improves it accordingly. | - 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 knows the international nature of the field of statistical science and data science. | - 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 explain ethical issues and dilemmas 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. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
The student knows the basics of statistical inference and probability and linear models.
|
|
|
|
|
In this course students will learn about some more advanced and state-of-the art statistical inference issues and techniques that are relevant for modern applications that go beyond the scope of traditional statistical methods :
-
prediction versus association
-
observational versus experimental studies
-
basics of causal inference and causal machine learning.
Examples (with R code) will also be discussed.
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Project ✔
|
|
|
|
|
|
|
|
Group work ✔
|
|
|
|
Paper ✔
|
|
|
|
Semester 1 (3,00sp)
| Evaluation method | |
|
| Written evaluation during teaching period | 50 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
|
| Written exam | 30 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
|
|
| Oral exam | 20 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
| Other | Discussion about the paper. |
|
|
|
|
|
| Use of study material during evaluation | ✔ |
|
| Explanation (English) | All course materials and own notations may be used. |
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | To get a pass mark, the student must pass for each of the following parts: (project, paper and oral exam). |
|
|
|
| Consequences | If the condition is not met, 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 | |
|
|
| Compulsory course material |
| |
Course notes or slides will be made available on Blackboard. |
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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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