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KARAKTERFORDELING · H 2025
13 kandidater
Kilde: Felles studentsystem (FS) via NTNU, slik den var publisert for høsten 2026. NTNU er ikke ansvarlig for innholdet på denne siden.
NTNU sin egen emnebeskrivelse — læringsutbytte, innhold og vurderingsform.
The onset of big data has revolutionised how governments and the business sector make decisions. Likewise, the increasing availability of historical quantitative information provides an excellent opportunity to fully exploit the analysis of large datasets to expand our knowledge of the past. This course provides an in-depth introduction to quantitative methods, covering some of the techniques most widely used in research in the historical and social sciences. Combining lectures and computer practicals, this hands-on course shows how to apply quantitative methods to historical information, both textual and numerical, keeping statistical theory and mathematics to a minimum. The goal is to provide students with the tools to critically engage with the literature relying on quantitative methods and to be able to conduct original research using those tools either in academia, the public or the business sector. In the process, students will master R, a statistical software widely used by practitioners in many different fields. Please note that if students plan to include this course in their coursework component, it must be preapproved by the relevant head of the study/PhD programme in cooperation with their supervisor.
More information can be found in the course website.
Kilde: Felles studentsystem (FS) via NTNU, slik den var publisert for høsten 2026. NTNU er ikke ansvarlig for innholdet på denne siden.
Hva burde andre vite før de tar dette? Anonymt, ingen innlogging.
Basert på hvor kandidatene til eksamen var registrert (DBH).
Emner som tas i de samme studieprogrammene som HIKU8863.
| Semester | Kandidater | Stryk |
|---|---|---|
| Høst 2025 | 13 | 0 % |
| Høst 2024 | 17 | 0 % |
| Høst 2023 | 14 | 28,6 % |
| Høst 2022 | 5 | 0 % |
| Høst 2021 | 6 | 0 % |
| Høst 2020 | 9 | 0 % |
| Høst 2019 | 10 | 0 % |