| Credits: 3,0 | | Study load hours: 81 | Period: semester 2 (3sp)  |
| Language of instruction: English | | Exam contract: not possible |
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This course introduces the student to the basics of epidemiology. The focus is on the methodology of the study of the
occurrence of health and disease in human (and by extent in animal) populations. The course starts with an overview of the history and present of epidemiology. Then, it introduces the measures of occurrence and association, and the concepts of causality and systematic error. Last, the main study designs used in epidemiology are discussed. In particular, the general concepts in relation to cohort, case-control and trials are explained.
By the end of this course, students should be able to:
·understand basic terminology and basic principles of epidemiology,
·calculate and interpret the different measures of occurrence, association and impact used in epidemiological research
·understand causality concepts and identify threats to causality in epidemiological studies (bias)
·accurately present different types of epidemiological studies.
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| Compulsory course material |
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Hand-outs, slides, selected readings |
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| Recommended reading |
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Semester 2 (3,00sp)
| Evaluation method | |
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| Written exam | 100 % |
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| Multiple-choice questions | ✔ |
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| Off campus online evaluation/exam | ✔ |
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| For the full evaluation/exam | ✔ |
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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 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 design methodology. | | | - DC
| ... correctly using state-of-the-art software. | - 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. | | | - DC
| The student is able to extract user tasks from domain experts. | - EC
| The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application. | | | - DC
| The student is able to correctly use the theory methodologically, thus contributing to scientific research within the field of statistical and data science.
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| The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of statistical and data science.
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| The student is capable of acquiring new knowledge. | - EC
| The student knows the relevant stakeholders and understands the need for assertive and empathic interaction with them. | | | - DC
| The student can respond to the interests of relevant stakeholders, particularly within the programme specialisation. | | | - 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. |
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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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