Master Class

Course, Msc Computer Science, Leiden University, 2022

The Master Class for Computer Science students takes place every second week and is mandatory for all students who are in their second year, i.e., in their research year (Specialisation courses and the Master’s Thesis Research Project). The Master Class aims at stimulating active interaction of students with their classmates, discussing open problems, issues, etc., and helping students to stay on track. Each student is asked to give a brief presentation in the Master Class about their Master’s Thesis Research Project. About the research topic and goals, the status and (expected) results. In addition, we will discuss topics such as the structure of a Master’s Thesis, writing scientific publications, presenting a scientific paper, time management and other soft skills. In addition to these there are a number of guest lectures from companies, PhD students and entrepeneurs to prepare you for the job market after you graduate.

Data Science course

Course, Bsc Computer Science, Leiden University, 2022

Data Science places data mining, machine learning and statistics in context, both experimentally and socially. If you want to correctly deploy data mining techniques, you must be able to translate a (broadly formulated) question by a customer or a co-worker into an experimental set-up, to make the right choices for the methods you use, and to be able to process the data in the right form to apply those methods. After performing your experiments, you should not only be able to evaluate the results but also interpret and translate it back to the original question (e.g. by visualization). Socially, data science is of great importance because the media simplify many data-driven results and statistical research, often making mistakes. Thus, a lot of nonsense comes down on us and it is up to you, the data scientists of the future, to recognize, explain and correct that nonsense. This course is a combination of lectures and practical sessions, in which you take a hands-on approach to solving real-world data science problems.