 Hello and welcome to our pitch. My name is Helito Stauffo and I'm working at the Department of Statistics and the Digital Science Centre at the University of Innsbruck in Austria. Over the next few minutes we would like to give you some insights into teaching and introductory programming course with R. This is joint work together with my colleagues Johanna Schimjak-Opoka, Luis Miguel Rodriguez and Achim Zeileis. The Digital Science Centre was only founded recently as part of the ongoing Digitalization Initiative and aims to foster interdisciplinary synergies between computer sciences, mathematics, statistics and other scientific disciplines across the university, both in research but also in teaching. Specifically the Digital Science Centre offers a new minor study called Minor Digital Science which is open for mostly all students from all faculties. The first course or entry course of this minor is an introduction to programming for students without any prior knowledge about programming at all. The students can choose between two different tracks by either learning Python or R, which we are focusing on in this pitch. Our course introduces the students to the basics from how to use an integrated development environment, how to write first scripts and small programs and solving practical exercises with increasing difficulty. This course focuses on the basics such as data types and classes, control flow and writing and testing functions almost exclusively using base R to learn the fundamental concepts of a programming language and getting used how to solve tasks programmatically. While data analysis touched on upon in some occasions it's mainly deferred to subsequent modules within the minor. In the first part the students learn about the core infrastructure of R and program flow which allows them to write smaller programs. The second half of the course focuses on additional data structures, methods and classes as well as how to import, export and manage data sets in R. Besides this rough structure the whole course follows a flipped classroom design which allows all our participants to learn and practice in his or her own pace. Each week the participants prepare some new content at home before coming to the class. The beginning of each class we do have a question and answer session which allows us to discuss and clarify certain aspects followed by a quiz where the students answer a series of knowledge questions within a specific time frame. The ship motivates them to properly prepare themselves for the class to get the best possible learning experience out of the practical exercises but also gives us some immediate feedback to identify possible problems or difficulties. The rest of the lecture is then used to occasionally present additional content and solving practical exercises. This is done in smaller groups out of four to five students where they're working together, helping each other, supervised by us as the lecturers. After class the students can then finish and refine their solutions and prepare the new content for the upcoming lecture. The entire course is actually centered around a new and freely available book which can be accessed on the website diskdown.org. The chapters of the book depict the different lectures of the course such that the students can easily follow the structure and read and prepare one chapter every week. If you have a look at our book you will notice that our book differs from most introductory material available for R as it is focusing on the basics mentioned previously. While the content, so what the students are doing is mainly based on base R, different technologies are used in and around the course. The freely available book for example is based on bookdown combined with some custom elements to include optional exercises the students can use for self-study. The R package R exams is also heavily used not only for the knowledge quizzes every week but also for both the midterm and end-term exam. R exam based closed questions allow to automatically evaluate parts of the examination and give immediate feedback to the students. In addition it also allows the students to upload their solution so the R script which is then manually evaluated by us. In addition we are also making use of tiny tests so alongside some of the exercises the students are working on during your class but also some exercises for the exam we provide additional tiny test test files. Our students can use these test files for self-assessment so to see where their code fails identify possible problems or bugs and fix them until they can pass all the tests. This helps keeping the frustration level low and also casually shows the importance of proper testing. Thank you very much for your interest in case you have any questions please join me on Tuesday afternoon or contact me at any time by a Twitter or email. Enjoy USR see you soon!