| Dozent/in |
Dr. rer. pol. Markus Johannes Meierer; Patrick Bachmann, MA |
| Veranstaltungsart |
Vorlesung |
| Code |
HS191580 |
| Semester |
Herbstsemester 2019 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Bachelor
Master |
| Termin/e |
Fr, 13.12.2019, 08:15 - 12:00 Uhr, HS 7
Sa, 14.12.2019, 08:15 - 12:00 Uhr, HS 10
Fr, 20.12.2019, 08:15 - 12:00 Uhr, HS 9
Sa, 21.12.2019, 08:15 - 12:00 Uhr, HS 9 |
| Umfang |
2 Semesterwochenstunden |
| Turnus |
blocked |
| 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 |
Managing the 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 version of your operating system installed). |
| Sprache |
Englisch |
| Anmeldung |
To attend the course / exercise, enrolment via the e-learning platform OLAT is required. Registration is possible from 2 to 27 September 2019. The students themselves are responsible for checking the creditability of the course to their degree programme. Direct link to the OLAT course: https://lms.uzh.ch/url/repositoryentry/16616980738 |
| Leistungsnachweis |
Multiple-choice tests, online exercises, group work |
| Abschlussform / Credits |
Multiple-choice tests, online exercises, group work / 3 Credits
|
| Hörer-/innen |
Ja |
| Kontakt |
markus.meierer@uz.ch |