Vor der Buchung beachten:

Bitte beachten Sie vor Ihrer Kursanmeldung unsere Allgemeine Teilnahmebedingungen (pdf, 92 KB) insbesondere aber unser Fairplay: An- und Abmelden (pdf, 299 KB).. Vielen Dank!

Covid-19 Schutzkonzept

Basisschutzkonzept der UZH


Hier finden Sie Antworten zu häufig gestellten Fragen.

Zum Abmelden von Kursen gehen Sie bitte auf Ihre Teilnehmenden-Homepage .


E-Mail: training@zi.uzh.ch


Wartungsarbeiten am 4.10.2022

Am 4.10. wird die Kursdatenbank migriert.

Eine Anmeldung zu den Kursen ist an diesem Tag auf der Webseite nicht möglich.
Sie können sich weiterhin von ihren Kursen auf Ihrer Teilnehmenden Homepage abmelden.
Wenn Sie sich zu einem Kurs anmelden möchten oder wenn Sie Fragen haben, melden Sie sich bitte an training@zi.uzh.ch

Vielen Dank für Ihr Verständnis.

Maintenance work on 4.10.2022

On 4.10. the course database will be migrated.

It will not be possible to register for courses on the website on this day.
You can still deregister from your courses on your participant homepage
If you would like to register for a course or if you have any questions, please contact training@zi.uzh.ch.

Thank you for your understanding.

R: Web Scraping

Collecting and preprocessing data is always the first step in a data analysis project or in a machine learning pipeline. The web plays a crucial role here: Often, authoritative statistical data are published as tables on regularly updated websites. Data found on social networks might provide valuable ground truth for training machine learning algorithms. However, gathering data from websites is often not that straightforward and requires an understanding of the architecture of the web.

In this course, you'll learn how to leverage R to collect and parse data found on various kinds of websites. By doing so, you'll get to know typical website architectures and how to approach them efficiently for scraping. The first part of the course will be held remotely and will introduce various concepts and R functions, while the second part will be held on site, where you'll be faced with some hands-on scraping challenges.

Learning Content

  • Overview of approaches for collecting data from a remote source
  • Introduction of different R packages for scraping (httr and rvest)
  • How to parse tabular data on websites into R data frames
  • Scraping best practices
  • Where to go from here & approaches for more complicated websites


Either some basic knowledge of R (ideally with the Tidyverse) or completion of the following courses.


ARE - R: Basic IntroductionARF - R: tidyverse for Data Science


Students and employees of the University of Zurich. This course is particularly suitable for students at the MSc-/PhD-Level as well as other academic personnel such as postdocs.


Handouts will be distributed during the course.


Kurs ARW 1
Freie Plätze:0
Dauer:4 Tag(e) / 12 Stunde(n)
Kursleitende:Timo Grossenbacher
Teilnehmerzahl:Min: 7
Max: 20
Ort:This course will be held using Zoom. The participants receive an e-mail with instructions from their course instructor
Zoom Meeting
Dienstag, 22. November 202217:00 - 20:00
Donnerstag, 24. November 202217:00 - 20:00
Dienstag, 29. November 202217:00 - 20:00
Donnerstag, 1. Dezember 202217:00 - 20:00
Veranstaltungs-Infos als ICS Feed