| Dozent/in |
Dr. Andrea De Angelis |
| Veranstaltungsart |
Kolloquialvorlesung |
| Code |
HS231419 |
| Semester |
Herbstsemester 2023 |
| Durchführender Fachbereich |
Politikwissenschaft |
| Studienstufe |
Bachelor
Master |
| Termin/e |
Di, 19.09.2023, 08:15 - 10:00 Uhr, 3.B58
Di, 26.09.2023, 08:15 - 10:00 Uhr, 3.B58
Di, 03.10.2023, 08:15 - 10:00 Uhr, 3.B58
Di, 10.10.2023, 08:15 - 10:00 Uhr, 3.B58
Di, 17.10.2023, 08:15 - 10:00 Uhr, 3.B58
Di, 24.10.2023, 08:15 - 10:00 Uhr, 3.B58
Di, 31.10.2023, 08:15 - 10:00 Uhr, 3.B58
Di, 07.11.2023, 08:15 - 10:00 Uhr, 3.B58
Di, 14.11.2023, 08:15 - 10:00 Uhr, 3.B58
Di, 21.11.2023, 08:15 - 10:00 Uhr, 3.B58
Di, 28.11.2023, 08:15 - 10:00 Uhr, 3.B58
Di, 05.12.2023, 08:15 - 10:00 Uhr, 3.B58
Di, 12.12.2023, 08:15 - 10:00 Uhr, 3.B58
Di, 19.12.2023, 08:15 - 10:00 Uhr, 3.B58 |
| Weitere Daten |
Selected chapters will be freely available through Perusall (the student code will be provided in class). Additional student material for Imai’s (2017) QSS book is available through the textbook website, and the tidyverse code of the book is made available by Jeffrey Arnold at this link. |
| Umfang |
2 Semesterwochenstunden |
| Turnus |
wöchentlich |
| Inhalt |
Welcome to “Introduction to Statistics for the Social and Political Sciences” — a beginner-friendly lecture designed to introduce descriptive and inferential statistics with a modern approach that values real-world applications and data literacy. If you are motivated to learn statistics and data science but feel insecure about your mathematical skills, this lecture is for you. Covering key topics in statistics and data science, such as data visualization, statistical sampling, descriptive statistics, inferential statistics, and regression modeling, this lecture gives you statistical tools to develop your solutions to analytical problems.
This lecture aims at creating an inclusive and supportive learning environment where all students feel comfortable asking questions and sharing their ideas. Thus, the course is designed to be accessible regardless of background and previous experience with statistical concepts. As a social science student, you will also benefit from real-world applications of statistical analysis to social phenomena, showing how to develop and test theories, and make policy recommendations. You will also gain the opportunity to practice and develop your statistical skills in a project of choice in your field of interest. This course is the perfect place to start your journey towards becoming a confident data analyst to succeed in your studies and future career.
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| E-Learning |
https://lms.uzh.ch/url/RepositoryEntry/17430413661 |
| Lernziele |
By the end of the course, active participants will:
1. understand foundational concepts in descriptive and inferential statistics;
2. develop data literacy and statistical programming skills (importing, transforming, visualizing, and modeling data to communicate key results);
3. apply statistical knowledge and data literacy to tackle real-world questions delivering data-driven solutions.
|
| Voraussetzungen |
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. |
| 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 |
Course evaluation is based on:
1. mandatory readings and discussion using the online Perusall platform (1/3 of the grade);
2. statistical programming exercises to be solved in pairs using R (1/3 of the grade);
3. a final, personal statistical data analysis due one week after the end of the seminar (1/3 of the grade).
It is not necessary to register for the exam. More information about the exams: www.unilu.ch/ksf/pruefungen
|
| Abschlussform / Credits |
Active participation (20%), 2 assignments (40%), final assignment (40%) / 0 Credits
|
| Hinweise |
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. Some basic statistics and programming skills (e.g., one previous course in statistics) are recommended but not required in the presence of a strong motivation to learn. |
| Hörer-/innen |
Nach Vereinbarung |
| Kontakt |
deangelis@ipz.uzh.ch |
| Material |
Reading material will be circulated using Perusall. Course participants should have a working laptop. In case a laptop is not available, participants should contact the lecturer to get free Rstudio cloud access. Please register on the course OLAT repository. |
| Literatur |
- Imai, Kosuke (2017). Quantitative Social Science: An Introduction. Princeton: Princeton University Press.
- Wickham, Hadley, and Garrett Grolemund (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. First edition.
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