The development of research software has become an important part of research projects in many areas of science and engineering. At the same time, increasing computational power in the area of high performance computing has made computationally challenging statistical tasks feasible and highly desirable in many application fields.
In this week-long summer school, we will therefore address these different aspects and familiarize you with the most essential paradigms of software development, which support the design of efficient, user-friendly, and sustainable software. In particular, we will focus on the scientific programming language Julia.
The summer school is organized around keynote presentations by invited Julia experts and many hands-on tutorials. First, a gentle introduction including packaging, testing, virtualization, interaction, and visualization will supply you with the essential skills you need to use Julia in your research. Afterwards, we build on these skills to implement computationally expensive statistical methods. In particular, we will focus on methods for regression and resampling using bootstrap and permutations. That is, methods addressing two of the most common challenges in statistics: estimation of the relationship between variables of interest and the quantification of uncertainty. You are invited to bring your own problem to apply the skills you learn in this summer school.
What to expect?
- Healthy mix of keynotes from invited lecturers and hands-on sessions
- Small course fee of only 250 EUR due to financial support by the Cluster of Excellence EXC 2075 "Data-Integrated Simulation Science (SimTech)"
- Surrounded by a welcoming social program
Is this summer school for you?
You are at the beginning of your PhD and want to start with a solid foundation in Research Software Engineering? Or you simply always wanted to learn more about Julia? This is your chance. And we do not require much pre-knowledge. You should have at least:
- intermediate level1 in one programming language (does not need to be Julia)
- Basics know-how of Git
1 While we know it is near impossible to gauge skills adequately, we still want to have some assurance on the minimal programming experience/skill level. We will ask to self-identify as Beginner (e.g. experience from courses, knows basic syntax), Advanced Beginner (did some analyses, basic plotting is no longer a problem), Intermediate (used it to complete a project/paper, can decode many of the error messages), Advanced (developed packages, can debug 90% of problems, helps others).