We present here some of our educational projects.

OSE Scientific Computing Course

The sound analysis of computational economic models requires expertise in economics, statistics, numerical methods, and software engineering. The course provides an overview of basic numerical methods for optimization, numerical integration, approximation methods, and uncertainty quantification. Students deepen their understanding of each of these topics in the context of a dynamic model of human capital accumulation using respy. The last segment of the course welcomes several guest lecturers from the industry and other academic departments to present on how computational models are used in their everyday work. Please see our course website for details.

Partners:  Philipp Eisenhauer, Gregor Böhl, Annica Gehlen, Janos Gabler
Funding: Excellence Strategy—TRA Modelling

OSE Data Science Course

The course introduces students to basic microeconometric methods, where the objective is to teach students how to perform and evaluate causal claims. By the end of the course, they should be able to apply the methods discussed in class and critically evaluate a research paper that is based on one of them. The course is heavily based on Python programming and makes use of its SciPy ecosystem, as well as Jupyter Notebooks. In the same line as the Scientific Computing Course, the Data Science course also welcomes guest Lecturers from both public and private sector who share their insights on the importance of data science analysis. Please check our course website for more information.

OSE Course Projects

For both the Scientific Computing and the Data Science course, students are required to work on their own projects independently. We have build a documentation that includes basic instructions as well as example projects from earlier iterations from the Data Science Course. Please direct to our project documentation for more information.

OSE Primer

We organize a computation primer, which is essentially an introduction to (Python) programming. The course covers basic concepts such as variables, loops, functions, plotting, basics of scientific programming and data analysis, and some perspectives. The last iteration can be found in our GitHub repository.