| Dozent/in |
Dr. Benjamin Schlegel |
| Veranstaltungsart |
Kolloquialvorlesung |
| Code |
HS251555 |
| Semester |
Herbstsemester 2025 |
| Durchführender Fachbereich |
Politikwissenschaft |
| Studienstufe |
Bachelor
Master |
| Termin/e |
Mi, 17.09.2025, 12:15 - 14:00 Uhr, 4.A05
Mi, 24.09.2025, 12:15 - 14:00 Uhr, 4.A05
Mi, 01.10.2025, 12:15 - 14:00 Uhr, 4.A05
Mi, 08.10.2025, 12:15 - 14:00 Uhr, 4.A05
Mi, 15.10.2025, 12:15 - 14:00 Uhr, 4.A05
Mi, 22.10.2025, 12:15 - 14:00 Uhr, 4.A05
Mi, 29.10.2025, 12:15 - 14:00 Uhr, 4.A05
Mi, 05.11.2025, 12:15 - 14:00 Uhr, 4.A05
Mi, 12.11.2025, 12:15 - 14:00 Uhr, 4.A05
Mi, 19.11.2025, 12:15 - 14:00 Uhr, 4.A05
Mi, 26.11.2025, 12:15 - 14:00 Uhr, 4.A05
Mi, 03.12.2025, 12:15 - 14:00 Uhr, 4.A05
Mi, 10.12.2025, 12:15 - 14:00 Uhr, 4.A05
Mi, 17.12.2025, 12:15 - 14:15 Uhr, HS 10
Mi, 17.12.2025, 12:15 - 14:00 Uhr, 4.A05 |
| Weitere Daten |
1st re-exam: Tuesday, 03.02.2026, 10:15 - 11:45h, HS 1
2nd re-exam: Tuesday, 10.02.2026, 10:15 - 11:45h, 3.B58 |
| Umfang |
2 Semesterwochenstunden |
| Turnus |
Zweiwöchentlich |
| Inhalt |
Statistics is an important skill in the social sciences for empirically testing our theoretical ideas. Statistics uses data to describe objects and ideas, and to draw conclusions about the population from random samples. If you are motivated to learn statistics and data science but feel insecure, this "Introduction to Statistics for the Social Sciences" is for you. The course is designed for beginners. The course emphasizes the application of ideas rather than mathematical proofs. As a student, you will learn the important statistical concepts while applying the learned knowledge using the statistical software R. R is open source and therefore free for everyone to use. If you are the type of person who wants to learn something without investing time, this course is not for you. To learn statistics it's important to practice every week. There will be exercises in OLAT to practice and apply the skills.
The course is structured as follows: In the first part we describe data. To describe data, we learn what data is and how to read data into R, how data looks like and how we can recode data to get it on the right scale, we learn and apply different descriptive statistics and plot the data. We then learn about different distributions and probability theory as a prerequisite for inferential statistics: What can we learn from a sample about the population from which it was drawn? We then apply inference statistics to simple hypothesis testing and the linear regression model. |
| Lernziele |
By the end of the course, active participants will:
1. Understand the scientific methods and their application and relevance in the social sciences.
2. Have basic R skills.
3. Know how to present and interpret statistical information.
4. Understand descriptive and inferential statistics and the difference between them. |
| Voraussetzungen |
An intrinsic motivation to learn statistics and data science is the only hard requirement for this course: passive listening-only and credit-oriented participation is discouraged since it undermines effective and durable learning. |
| Sprache |
Englisch |
| Begrenzung |
Teilnahmebeschränkung vorbehalten: Studierende ab dem 3. Semester werden bevorzugt. |
| Anmeldung |
***Important*** In order to acquire credits, it is mandatory to register for the course via the UniPortal. Registration opens two weeks before and ends two weeks after the start of the semester. Registrations and cancellations are no longer possible after this period. The exact registration dates can be found here: http://www.unilu.ch/ksf/semesterdaten |
| Prüfung |
Final written exam on Wednesday, 17.12.2025, 12:15 - 13:45h
Course evaluation is based on:
weekly mini-exams (10%) and final exam (90%) |
| Abschlussform / Credits |
Benotete schriftliche Prüfung / 3 Credits
|
| Hinweise |
Bachelor / Master. The course is recommended for BA students in their higher (3+) semesters and is open to MA students. The registration via the e-learning platform OLAT is required to attend the lecture. Course participants should check if they are eligible for credits given their study program. |
| Hörer-/innen |
Nach Vereinbarung |
| Kontakt |
benjamin.schlegel@doz.unilu.ch |
| Material |
Course participants should have a working laptop. Please register for the course on the OLAT repository. |
| Literatur |
• Schlegel, Benjamin (2023). R for Social Scientists. (will be made available on OLAT)
• Wickham, Hadley, and Garrett Grolemund (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. First edition. Sebastopol, CA: O'Reilly. The textbook is freely available at: https://r4ds.had.co.nz/index.html
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