| Language of instruction : English |
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Sequentiality
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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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| 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
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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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Period 1 Credits 3,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 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 ✔
|
|
|
|
Period 1 Credits 3,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 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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|
|
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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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