About This Course

The course introduces students to the specific concepts of research integrity that are related to the practice of mathematical modeling and machine learning. This includes ethical principles of statistical practice, reproducibility, good computational practice and computational efficiency. Besides theoretical insights into the foundations of computational practice students acquire practical skills using modern computational tools (e.g. dynamic reporting, version control, containerization) and they start to learn about general programming techniques (e.g. unit tests, debugging, job scripting, parallelization). The module provides insights and practical tools that are useful throughout the curriculum of bachelor and master programs focused on quantitative research.

The course is taught in a flipped classroom setting: students are required to learn about concepts using provided material and complete assignments before an in-person session. Assignments and the in-person session contain peer and staff feedback and assessment.

• understand the link between good computational practice, scientific integrity and reproducibility
• have the necessary technical skills for reproducible research work, e.g. dynamic reporting, version control, containerization
• master the most relevant techniques of practical computing in mathematical modeling and machine learning, e.g. scripting approaches, simulations, good coding practice
• know the basics of programming techniques such as debugging, unit testing, distributed and parallel computing