| 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 | P2 SBU | P2 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 1st year Master Data Science - distance learning | Compulsory | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical |  |
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| | | Learning outcomes |
- 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 apply basic principles regarding ethics and integrity to the fields of statistics and data science. | | | - DC
| The student can explain ethical issues and dilemmas within the fields of statistics and data science. | - EC
| The student knows the relevant stakeholders and understands the need for assertive and empathic interaction with them. | | | - DC
| The student can reflect on the role of the statistician and data scientist in the interaction with the stakeholders. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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In this course, we will touch upon different capita selecta of data science, ranging from helicopter-view topics such as wicked problems and ethics, to specific such as software carpentry.
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Distance learning ✔
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Small group session ✔
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Period 2 Credits 4,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 60 % |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| 1st year Master Bioinformatics - distance learning | Optional | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
| 1st year Master Biostatistics - distance learning | Optional | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
| 1st year Master Quantitative Epidemiology - distance learning | Optional | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- 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 apply basic principles regarding ethics and integrity to the fields of statistics and data science. | | | - DC
| The student can explain ethical issues and dilemmas within the fields of statistics and data science. | - EC
| The student knows the relevant stakeholders and understands the need for assertive and empathic interaction with them. | | | - DC
| The student can reflect on the role of the statistician and data scientist in the interaction with the stakeholders. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
In this course, we will touch upon different capita selecta of data science, ranging from helicopter-view topics such as wicked problems and ethics, to specific such as software carpentry.
|
|
|
|
|
|
|
|
|
Distance learning ✔
|
|
|
|
Small group session ✔
|
|
|
|
Period 2 Credits 4,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 60 % |
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|
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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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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