Sat - Sat 11 Apr 2026 - 27 Jun 2026Past

Seminar -Advanced R

Training Hybrid (University of Duisburg-Essen)
Seminar Statistics R By DKZ.2R
More Info →
Registration Closed

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/

Tags:

Related Posts

Data Cafe Essen

The DKZ.2R team would like to announce that our next Data Café will take place on October 28th at the University Mensa at University Duisburg-Essen in Essen!

This event is designed to provide informal consulting opportunities for students, PhD’s, and early PostDocs. We’ll be here to provide quick answers on topics like:

  • How to structure your research files in a logical way
  • Second opinions on your statistical analysis or data visualization
  • What kinds of machine learning methods could be used for your project
  • Getting started with research data management
  • And anything else you might have questions about!

As a bonus, each consultation comes with a free refreshement!

Read More

Intermediate Python Topics

DKZ.2R presents the next installation of our carpentries-style workshop on intermediate python on the 24th and 26th of June at RWTH Aachen. Register now and learn more about Virtual Environments, Classes and Object Oriented Programming, Creating Modules, Static Code Analysis, Unit Testing and Publishing

Read More

Seminar - Statistical and Machine Learning Methods

Course Description and Goals:

Statistical learning is a field that teaches students how to analyze and interpret data by applying statistical methods and machine learning algorithms to uncover patterns, make predictions, and gain insights from data. The syllabus includes: • Statistical and machine learning methods, including linear and polynomial regression, logistic regression, and linear discriminant analysis. • Model validation techniques such as cross-validation and bootstrap, model selection, and regularization methods (ridge and lasso). • Nonlinear models, splines, and generalized additive models. • Tree-based methods, including random forests and boosting. • Support-vector machines and an introduction to causal inference. • Unsupervised learning methods such as principal components analysis and clustering (k-means and hierarchical). Examination Format: Report and Presentation. Further information, including locations, and Zoom links, can be found on our homepage: https://oek.wiwi.uni-due.de/studium-lehre/lehrveranstaltungen/sommersemester-26/statistical-learning-vorlesung-17350/

Read More