| Credits: 3,0 | | Study load hours: 81 | Period: semester 2 (3sp)  |
| Language of instruction: English | | Exam contract: not possible |
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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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Lecture ✔
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Small group session ✔
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Semester 2 (3,00sp)
| Evaluation method | |
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| Written evaluation during teaching period | 60 % |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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Learning outcomes | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
Master of Statistics and Data Science
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- 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
| 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 |
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| Included in these programmes | Tolerance3 |
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Y
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.15.1, section 3. |
3 Education, Examination and Legal Position Regulations art.16.9, section 2.
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