By DKZ.2R: Seminar -Advanced R

Date: Apr 11, 2026 - Jun 27, 2026
Category: Training
Location: Hybrid Format - University of Duisburg-Essen & Online
Seminar Statistics R

Course Description and Goals:

The course teaches advanced topics in R programming that become increasingly relevant for everyday applications in both applied and theoretical econometrics and empirical economics. It covers, amongst other topics:

• Advanced programming concepts, including object orientation, profiling, and debugging. • Packages for modern applications in data science. • Cutting-edge R extensions, for example for parallel computing and C++ integration. • Applications relevant to empirical economics and econometrics.

Admission and Formalities:

• The number of participants is limited. • Please apply in advance by emailing a one-page letter of motivation to Martin Schmelzer (schmelzer.martin@gmx.de) by 1 April 2026. • Please state your study program in the application. • Additionally, completion of a self-assessment is mandatory; however, the outcomes are not relevant for the admission decision.

General Information:

University: University of Duisburg-Essen • Study Program and Level: Master’s students, solid working knowledge of basic R programming is required. • Weekly Hours: The course is offered as a block course with integrated lecture and exercise sessions. • Course dates and times: 9:30–17:00 on • 11.04.2026 – A-003 • 18.04.2026 – A-003 • 25.04.2026 – A-003

• 09.05.2026 – S06 S00 B08 • 23.05.2026 – S06 S00 B08 • 29.05.2026 – S06 S00 B08 • 30.05.2026 – S06 S00 B08 • 27.06.2026 – S06 S00 B08

• Language: English

Further information, including locations, and Zoom links, can be found on our homepage: https://oek.wiwi.uni-due.de/studium-lehre/lehrveranstaltungen/sommersemester-26/advanced-r-for-econometricians-lecture-with-integrated-exercise-17359/

Related Posts

Do's and Don'ts in Research Data Management

Do's and Don'ts in Research Data Management

Research Data Management Do’s and Don’ts - Step up your RDM skills!

1. Structuring and naming your folders There is an easy way to make your data findable for you and your team: establish a folder structure which makes sense for you and your working group as well as naming conventions for your folders.

Don’t:

Paul and Suzie
»Guideline
>application
»version2_final
»v.3
»review
»3rd.version
>JD
»qn
»0-1

Instead do:

000_int_orga
»01_application
»02_review 120_questionaires
»01_qualitative »02_quantitative 130_data
»01_qualitative »02_quantitative

Read More
How To: Open Science

How To: Open Science

Tired of Recreating someone else’s work? - How Open Science can accelerate research and overcome reinvention

Have you ever found papers on algorithms but their implementation is missing? Found an interesting analysis but there is no way to check the results, as you don’t have access to the data they were derived from? Ever thought you had a great idea for a project, just to find out a year later that you are not the only research group following that specific idea? Not having access to other people’s code, data, metrics or even their plans for research projects often leads to unnecessary delays and scientific redundancies. There is an easy solution to overcome (almost) all of these issues. It’s called Open Science! What is Open Science? The UNESCO defines Open Science as a construct of “movements and practices aiming to make multilingual scientific knowledge openly available, accessible and reusable for everyone, to increase scientific collaborations and sharing of information for the benefits of science and society, and to open the processes of scientific knowledge creation, evaluation and communication to societal actors […]”. To ensure that everyone has access to scientific knowledge and infrastructure, Open Science focuses on four main concepts.

Read More
Documentation From User Experience

Documentation From User Experience

This post is a condensed version of a talk at our Data Compentcy College

If you regularly use scientific software written by others, or tried to replicate interesting research that relies on software, you have probably also invested weeks of work to solve a software problem or even given up on a software because of missing documentation. Finding a project that might be the solution to your problem and then failing to run the code is frustrating. Being unable to run a project you have built yourself years ago is even worse. Having experienced all those setbacks myself in the past I want to use this post to channel that frustration to fuel solutions for better documentation for our current and future projects.

Read More