Dozent/in |
Dr. Benjamin Schlegel |
Veranstaltungsart |
Kolloquialvorlesung |
Code |
HS241503 |
Semester |
Herbstsemester 2024 |
Durchführender Fachbereich |
Politikwissenschaft |
Studienstufe |
Bachelor
Master |
Termin/e |
Mi, 18.09.2024, 12:15 - 16:00 Uhr, HS 4 Mi, 25.09.2024, 12:15 - 16:00 Uhr, HS 6 Mi, 09.10.2024, 12:15 - 16:00 Uhr, ZOOM Mi, 23.10.2024, 12:15 - 16:00 Uhr, HS 6 Mi, 06.11.2024, 12:15 - 16:00 Uhr, HS 6 Mi, 20.11.2024, 12:15 - 16:00 Uhr, HS 6 Mi, 04.12.2024, 12:15 - 16:00 Uhr, HS 6 |
Umfang |
2 Semesterwochenstunden |
Turnus |
Zweiwöchentlich |
Inhalt |
Welcome to “Introduction to Statistics for the Social 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.
|
E-Learning |
https://lms.uzh.ch/url/RepositoryEntry/17583866539 |
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 |
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. |
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: www.unilu.ch/ksf/semesterdaten |
Prüfung |
No final exam in presence.
Course evaluation is based on:
Data Essay at the end of the semester
|
Abschlussform / Credits |
Essay / 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 (in addition to the official registration via the UniPortal). Course participants should check if they are eligible for credits given their study program.
Link for Zoom session on October 9, 2024: https://uzh.zoom.us/j/62617666925?pwd=9SEuCjTd1RVVBMB1LcLgiV5VmknDvX.1 |
Hörer-/innen |
Nach Vereinbarung |
Kontakt |
benjamin.schlegel@uzh.ch |
Material |
Reading material will be circulated using OLAT. Course participants should have a working laptop. Please register on the course OLAT repository:
https://lms.uzh.ch/url/RepositoryEntry/17583866539 |
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. |