Scientists spend a significant amount of time writing, maintaining and debugging software. While techniques for doing this efficiently have evolved, only few scientists have been trained to use them. They end up being overwhelmed by the coding challenges they face and keep re-inventing the wheel. In the hope to free more time for research they increasingly rely on generative AI, which may produce runnable code without the need to fully understand it. Such code can neither be reused nor trusted to work correctly and, as a result, scientists are hesitant to share it and publish it. In this course we will present a selection of advanced programming techniques and best practices which are standard in the industry, but especially tailored to the needs of a programming scientist. Lectures are interactive and allow participants to acquire direct hands-on experience with the topics. Participants will work in pairs throughout the school and will team up to practice the newly learned skills in a real programming project — an entertaining computer game. We use the Python programming language for the entire course. This school is targeted at PhD students, postdocs and more senior researchers from all fields, who do a lot of programming in their academic life, but were never taught how to do so properly. Competence in programming and basic knowledge of Python and git is assumed. Participants are required to work through the proposed introductory material before.