| Dozent/in |
Prof. Dr. Markus Johannes Meierer |
| Veranstaltungsart |
Vorlesung |
| Code |
HS261044 |
| Semester |
Herbstsemester 2026 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Bachelor
Master |
| Termin/e |
Mo, 24.08.2026, 09:15 - 14:00 Uhr, 4.B55
Di, 25.08.2026, 09:15 - 14:00 Uhr, 4.B55
Mi, 26.08.2026, 09:15 - 14:00 Uhr, 4.B55
Do, 27.08.2026, 09:15 - 14:00 Uhr, 4.B55
Fr, 28.08.2026, 09:15 - 14:00 Uhr, 4.B55 |
| Umfang |
Blockveranstaltung |
| Turnus |
Block course |
| Inhalt |
People that use data analytics often spend more than 80% of their time with collecting, cleaning, and organizing data and only 20% with applying statistical models. This is not only true for real world analytics, but also for data analyses within bachelor/master theses. This class will prepare you for those challenges by applying a non-technical approach.
This class provides a hands-on introduction to Python for data management. We explain data wrangling techniques that "scale well", i.e. that are applicable to sizeable real-world datasets. Further, we present automatization techniques, which help to save time in programming projects and reduce the number of bugs.
This class is a lecture with integrated exercises. For every session, you are required to bring your laptop (with the latest version of your operating system installed). We do not require any experience with Python as we start from the very beginning (i.e. installing Python). However, we do require the willingness to actively participate and contribute to the class. No statistical models (besides mean and standard deviation) will be discussed in this class. |
| Lernziele |
Be able to manage data in Python:
- loading external data (from text files, Excel files, databases)
- merging, aggregating, and selecting observations
- simplifying complex and repetitive tasks |
| Voraussetzungen |
Bring a laptop (with the latest operating system version installed). |
| Sprache |
Englisch |
| Anmeldung |
Binding registration takes place via the UniPortal; see notes below in the «Proof of Performance» field.
For course information and materials, registration on the OLAT e-learning platform is required from 17 – 23 August 2026. The students themselves are responsible for verifying the course’s creditability towards their degree program.
Direct link to the OLAT course: https://lms.uzh.ch/url/RepositoryEntry/17903616191 |
| Leistungsnachweis |
Daily examinations during the course of the block course.
***IMPORTANT*** To receive course credit and earn academic credits, registration via the Uni Portal from 17 August (starting at 9:00 a.m.) – 23 August 2026 is MANDATORY.
Late registrations and withdrawals will not be accepted. Once the registration period has ended, participation in the course is MANDATORY. If the course is not completed without a valid reason and without proper withdrawal (including supporting documentation; see the Exam Guidelines), the course will be considered failed (grade 1). |
| Abschlussform / Credits |
multiple-choice exams on programming exercises and theory / online exercises / 3 Credits
|
| Hinweise |
Lecture with integrated exercises (details are announced during the kick-off session on course logistics). |
| Hörer-/innen |
Nein |
| Kontakt |
markus.meierer@doz.unilu.ch |