Digital Skills Programme

The workshops offered by the Digital Skills Programme can now be found in the regular course catalogue. Type “Digital Skills” in the title search! The course archive can be found below.

The University of Lucerne offers its students courses to strengthen their digital skills. Courses are open to Master and doctoral students of all faculties as well as postdoctoral researchers at the University of Lucerne. They are particularly recommended for students of the programme Lucerne Master in Computational Social Sciences (LUMACSS).

Course Archive

Nicolas Attalides

Course dates:

Friday, 27 February 2026, 9.30 a.m. to 4.30 p.m.
Saturday, 28 February 2026, 9.30 a.m. to 4.30 p.m.

Credits: 1 ECTS
Mode: online
Limitation: max. 15 participants, first come, first serve

Further information

PLEASE NOTE: NO PRIOR KNOWLEDGE IS REQUIRED.

Registration

Luigi Curini

Course dates:

Friday, 20 March 2026, 9.15 a.m. to 4.45 p.m. - online
Saturday, 21 March 2026, 9.15 a.m. to 4.45 p.m. - online
Friday, 27 March 2026, 9.15 a.m. to 4.45 p.m. - online
Saturday, 28 March 2026, 9.15 a.m. to 4.45 p.m. - online

Credits: 2 ECTS
Mode: block online
Limitation: max. 15 participants, first come, first serve

Further information

Please note: Course participants are expected to have some basic knowledge of the R programming language with some basic data analysis skills.

Registration

Nicolas Attalides

Course dates:

Saturday, 17 April 2026, 9.30 a.m. to 4.30 p.m.
Saturday, 18 April 2026, 9.30 a.m. to 4.30 p.m.

Credits: 1 ECTS
Mode: online
Limitation: max. 15 participants, first come, first serve

Further information

Please note: Course participants are expected to have some basic knowledge of the R programming language with some basic data analysis skills.

Registration

Nicolas Attalides

Course dates:

Friday, 24 April 2026, 9.30 a.m. to 4.30 p.m.
Saturday, 25 April 2026, 9.30 a.m. to 4.30 p.m.

Credits: 1 ECTS
Mode: online
Limitation: max. 15 participants, first come, first serve

Further information

Please note: Course participants are expected to have some basic knowledge of the R programming language.

THIS COURSE IS FULLY BOOKED! You can register in order to be put on a waiting list.

Resul Umit

Course dates:

Friday, 8 May 2026, 9.15 a.m. to 4.30 p.m. - in-person, room 3.A05
Saturday, 9 May 2026, 9.15 a.m. to 4.30 p.m. – in-person, room 3.A05

Friday, 15 May 2026, 9.15 a.m. to 4.30 p.m. - online
Saturday, 16 May 2026, 9.15 a.m. to 4.30 p.m. - online

Friday, 22 May 2026, 9.15 a.m. to 4.30 p.m. - online
Saturday, 23 May 2026, 9.15 a.m. to 4.30 p.m. - online

Credits: 3 ECTS
Mode: 1st block in-person, other blocks online
Limit: Max. 15 participants, first come, first serve

Further information

PLEASE NOTE: Participants should have basic knowledge of the R programming language, such as working with data frames and basic functions.

Registration

Nicolas Attalides

Course dates:

Friday, 8 May 2026, 6 December, 9.30 a.m. to 4.30 p.m.
Saturday, 9 May 2026, 9.30 a.m. to 4.30 p.m.
Saturday, 30 May 2026, 9.30 a.m. to 4.30 p.m.

Credits: 1.5 ECTS
Mode: online
Limitation: Max. 15 participants, first come, first serve

Further information

Please note: If you want to work within a data team, SQL is the must-have tool! No prior knowledge of SQL is required. Depending on time we might use SQL within R. No prior knowledge of R programming or RStudio is required.

THIS COURSE IS FULLY BOOKED! You can register in order to be put on a waiting list.

Nicolas Attalides

Course dates:

Friday, 26 September, 9.30 a.m. to 4.30 p.m. - on-site in room 4.B54
Saturday, 27 September, 9.30 a.m. to 4.30 p.m. - on-site in room 4.B54

Friday, 3 October, 9.30 a.m. to 4.30 p.m. - online
Friday, 10 October, 9.30 a.m. to 4.30 p.m. - online

Credits: 2 ECTS
Mode: first block in-person (room 4.B54), second block online
Limit: Max. 15 participants, first come, first serve

Further information

Please note: Course participants are expected to have a good working knowledge of the R programming language. It is assumed that participants have some prior experience in basic data analysis (such as data manipulation and visualisation) and a basic understanding of statistics.

Registration is closed!

Nicolas Attalides

Course dates:

Saturday, 4 October, 9.30 a.m. to 4.30 p.m.
Saturday, 11 October, 9.30 a.m. to 4.30 p.m.

Credits: 1 ECTS
Mode: online
Limit: Max. 15 participants, first come, first serve

Further information

Please note: Basic skills in programming with R are required. Ideally, you have attended an “Introduction to R” course or have been coding in R for a few months. This course does not require any previous knowledge of statistics.

Registration is closed!

Resul Umit

Course dates:

Friday, 17 October, 10 a.m. to 4.30 p.m.
Saturday, 18 October, 10 a.m. to 4.30 p.m.

Friday, 24 October, 10 a.m. to 4.30 p.m.
Saturday, 25 October, 10 a.m. to 4.30 p.m.

Credits: 2 ECTS
Mode: online
Limit: Max. 15 participants, first come, first serve

Further information

Please note: Participants should have basic knowledge of the R programming language, such as working with data frames and basic functions.

Registration is closed!

Nicolas Attalides

Course date:

Saturday, 8 November, 9.30 a.m. to 4.30 p.m.

Credits: 0.5 ECTS
Mode: online
Limit: Max. 15 participants, first come, first serve

Further information

Please note: Course participants are expected to have some basic knowledge of the R programming language. Prior experience in basic data analysis (such as data manipulation and visualisation) or writing functions would help the learning experience but is not required.

Registration is closed!

Emma Hoes & Jonathan Klüser

Course dates:

Friday, 14 November, 9.15 a.m. to 4.30 p.m. - on-site in room HS 3
Saturday, 15 November, 9.15 a.m. to 4.30 p.m. - on-site in room 3.B57

Friday, 21 November, 9.15 a.m. to 4.30 p.m. - on-site in room 1.507 (1. floor above Baloo)
Saturday, 22 November, 9.15 a.m. to 4.30 p.m. - on-site in room 3.B47

Credits: 3 ECTS
Mode: on-site
Limit: Max. 15 participants, first come, first serve

Further information

Please note: No background in experimental methods required, but Students should be familiar with basic research design and be comfortable working in R.

Registration is closed!

Nicolas Attalides

Course dates:

Saturday, 6 December, 9.30 a.m. to 4.30 p.m.
Saturday, 13 December, 9.30 a.m. to 4.30 p.m.

Credits: 1 ECTS
Mode: online
Limit: Max. 15 participants, first come, first serve

Further information

Please note: If you want to work within a data team (as a scientist, engineer or analyst), SQL is the must-have tool! No prior knowledge of SQL is required.

Natalia Umansky

Course dates:

25 January 2024, 9.30 a.m. to 5.00 p.m.
26 January 2024, 9.30 a.m. to 5.00 p.m.
27 January 2024, 9.30 a.m. to 5.00 p.m.

Digital Skills Programme

The workshops offered by the Digital Skills Programme can now be found in the regular course catalogue. Type “Digital Skills” in the title search! The course archive can be found below.

Course Archive

Spring Term 2026

Introduction to R

PLEASE NOTE: NO PRIOR KNOWLEDGE IS REQUIRED.

Text Classification Algorithms: From Bag-of-Words to Dynamic Word Embeddings

Please note: Course participants are expected to have some basic knowledge of the R programming language with some basic data analysis skills.

Introduction to Visualisations in R

Please note: Course participants are expected to have some basic knowledge of the R programming language with some basic data analysis skills.

Introduction to Shiny in R

Please note: Course participants are expected to have some basic knowledge of the R programming language.

THIS COURSE IS FULLY BOOKED! You can register in order to be put on a waiting list.

Causal Analysis with R

PLEASE NOTE: Participants should have basic knowledge of the R programming language, such as working with data frames and basic functions.

Introduction to SQL

Please note: If you want to work within a data team, SQL is the must-have tool! No prior knowledge of SQL is required. Depending on time we might use SQL within R. No prior knowledge of R programming or RStudio is required.

THIS COURSE IS FULLY BOOKED! You can register in order to be put on a waiting list.

Fall Term 2025

Introduction to Machine Learning in R

Please note: Course participants are expected to have a good working knowledge of the R programming language. It is assumed that participants have some prior experience in basic data analysis (such as data manipulation and visualisation) and a basic understanding of statistics.

Registration is closed!

Introduction to Statistics in R

Please note: Basic skills in programming with R are required. Ideally, you have attended an “Introduction to R” course or have been coding in R for a few months. This course does not require any previous knowledge of statistics.

Registration is closed!

Bluesky Data: Collection, Cleaning, and Analysis

Please note: Participants should have basic knowledge of the R programming language, such as working with data frames and basic functions.

Registration is closed!

Introduction to Building Packages in R and Version Control

Please note: Course participants are expected to have some basic knowledge of the R programming language. Prior experience in basic data analysis (such as data manipulation and visualisation) or writing functions would help the learning experience but is not required.

Registration is closed!

Experimental (Survey) Methods in Social Sciences

Emma Hoes & Jonathan Klüser

Please note: No background in experimental methods required, but Students should be familiar with basic research design and be comfortable working in R.

Registration is closed!

Introduction to SQL

Please note: If you want to work within a data team (as a scientist, engineer or analyst), SQL is the must-have tool! No prior knowledge of SQL is required.

Spring Term 2025

Introduction to Web Scraping and Text Analysis in R

Classification Algorithms in Text Analytics

Introduction to Shiny in R

Telling stories with Data - A Hands-on Introduction to Data Journalism

Fall Term 2024

TikTok: Theories, Tools, and Techniques for Content Analysis

Introduction to Machine Learning in R

Introduction to Statistics in R

Introduction to Visualisations in R

Introduction to Building Packages in R and Version Control

Spring Term 2024

Automated Image and Video Analysis

Introduction to R

Introduction to Shiny in R

Big Data Analytics

Data wrangling and visualization with Tidyverse

Introduction to Web Scraping and Text Analysis in R

Introduction to Machine Learning in R

Introduction to Visualisations in R

Introduction to Building Packages in R and Version Control

Introduction to Statistics in R

Fall Term 2023

Introduction to Shiny in R

Introduction to Web Scraping and Text Analysis in R

Experimental (Survey) Methods in Social Sciences

Automated Image and Video Analysis

Information Visualizations in R

Building packages in R and version control

Spring Term 2023

Big Data Analytics

Introduction to Shiny in R

Introduction to web scraping and text analysis in R

Information Visualizations in R

Fall Term 2022

Introduction to Shiny in R

Building packages in R and version control

Information Visualizations in R

Introduction to web scraping and text analysis in R

Spring Term 2022

Web-Scraping with R

Working with Twitter Data in R

Big Data Analytics