Lernen Sie, wie Sie bessere wissenschaftliche Software entwickeln

Compact Course “The Unix Shell + Version Control with Git”
Dr. Dominic Kempf

The Unix shell is a powerful tool that allows people to do complex things with just a few keystrokes. More importantly, it helps them combine existing programs in new ways and automate repetitive tasks so they aren’t typing the same things over and over again. Use of the shell is fundamental to using a wide range of other powerful tools and computing resources. The course will include hands-on live coding sessions where participants exercise the learned commands on their own computers.

Version control is the lab notebook of the digital world: it is used to keep track of what was done and to collaborate with other people. Its use is the state of the art in software development projects of all scales. However, it is not limited to software: books, papers, small data sets, and anything that changes over time or needs to be shared can and should be stored in a version control system. The course will include hands-on live coding sessions where participants exercise the learned commands on their own computers.

Compact Course “Intermediate Topics in Version Control with Git”
Dr. Dominic Kempf

Applying version control can be a game changer for a collaborative research software project. However, even in projects that already successfully use Git, there is often room for improvement of the employed Git workflows. This workshop tries to delve into collaborative workflows using Git branches and discuss their strengths. Special emphasis is given to the resolution of merge conflicts that may be required in the process of dealing with diverging branches.

Compact Course “Expert Topics in Version Control with Git”
Dr. Dominic Kempf

Applying version control can be a game changer for a collaborative research software project. However, even in projects that already successfully use Git, there is often room for improvement of the employed Git workflows. This workshop explores some advanced topics that increase the participants’ repertoire of Git workflows: Bisection, Worktrees, Submodules, Large File Storage and Pre-commit Hooks. Additionally, we showcase invasive methods of modifying a Git repository via Rebase.

Compact Course “Python best practices”
Dr. Inga Ulusoy

Python has rapidly advanced to the most popular programming language in science and research. From data analysis to simulation and preparation of publications, all can be done in Python with appropriate libraries and implementing own modules. We will discuss Python Enhancement Proposals (PEP) and how these can help you write cleaner code. Common pitfalls in Python will be explained with examples. We will demonstrate typical “bad programming” and how to code the examples in a more pythonic way.

Compact Course “ Generative AI for writing (research) software”
Dr. Inga Ulusoy

Generative AI is emerging as a major creative force that supports humans in content creation. Specifically trained models can support software developers with their software projects and lead to time savings and a shift in what aspects of generating software are more important on a day-to-day basis. In this course, we will learn about GitHub Copilot, the differences to ChatGPT, and how to set up and use GitHub Copilot in coding projects. Best practices in using Copilot, as well as recommendations how to use it efficiently and safely will be introduced.

Compact Course “AI in research software: Best practices for developing and using ML models”
Dr. Inga Ulusoy

The AI revolution is moving even more rapidly than the digital revolution and leads to the emergence of completely new tools and technologies that affect the scientific process. In this course, we will learn about data-based research software, tools and communities that are relevant in creating and sharing such software, and about best practices in training machine-learning models. Research software that is based on ML models requires an additional layer of best practices in the implementation, including testing of non-deterministic processes. Security aspects as well as bad examples are discussed to highlight the importance of adhering to a best practices code of conduct.

Compact Course “Data Exploration with Python and Jupyter”
Dr. Liam Keegan

Jupyter notebooks are a great tool for exploring and interacting with data using the Python programming language and its rich ecosystem of libraries. In this course we will cover basic usage of the Pandas library to download a dataset, explore its contents, clean up missing or invalid data, filter the data according to different criteria, and plot visualizations of the data.

Compact Course “Effective software testing”
Dr. Liam Keegan

A good test suite makes extending, maintaining and debugging a codebase both easier and faster. In this course we will look at the different kinds of tests, and understand how to write good tests. We will also cover different testing strategies, such as test-driven-design when writing new code, or acceptance testing when working with legacy code that doesn’t have a good test suite. Code samples will use the Python testing framework pytest but the concepts also apply to other frameworks and languages.

Praktikum “Beginner/Advanced Software Practical Research Software Engineering”
Dr. Dominic Kempf

The software practical is a mandatory course in the BSc/MSc Computer Science curriculum, as well as in Mathematics and Scientific Computing. Students will carry out project work over the course of a semester, implementing a self-contained project according to best practices of software development. Project ideas will be presented on a first meeting at the beginning of the semester and are generally taken from the daily work of the Scientific Software Center.

The SSC version of the practical focusses on "Research Software Engineering". RSE is a rather new term that describes the use of software developement and software engineering practices in research applications. In the practical, we will implement tools that support research software development. Projects are taken from actual projects by the SSC or are inspired from such.

The primary programming language for the practical is Python, although C++ and JavaScript/TypeScript might be sprinkled in depending on the project.

If you are interested in the practical, please sign up in MÜSLI. You will then receive an invitation to a meeting on April 11th, 10 AM. At this meeting, we will listen to the project presentation from last semester and projects for the upcoming semester are presented.