COURSE CURRICULUM

Leiden University’s master’s specialisation in Astronomy and Data Science is a two-year program designed to combine advanced astronomical research with the development and application of modern computational techniques. At the start of the program, students draw up a Master Study Plan in consultation with the Study Advisor, outlining the courses and research projects for the subsequent two academic years. The curriculum integrates a solid foundation in both astronomy and computer science, preparing students to work with the large, complex datasets typical of contemporary astronomical research.The coursework component consists of mandatory computer science courses (24 EC), elective astronomy courses (36 EC), and specialized astronomy courses divided into core courses (at least 18 EC), instrumentation-related courses (at least 6 EC), and additional astronomy courses (12 EC). These courses provide both the theoretical background and practical skills necessary to understand astronomical phenomena and to apply computational methods, including machine learning and data mining, to real-world data.Research is a central focus of the program, consisting of two major projects. The first research project (30 EC) introduces students to independent research in general astronomy, while the Master’s Research Project (30 EC) emphasizes data science applications in astronomy, allowing students to work on original, data-intensive research under supervision.Educational methods include lectures, working groups, practical classes, lab work, individual papers, presentations, research projects, and colloquia. Assessment is conducted through written and oral exams, homework assignments, presentations, reports, and the research projects.Graduates of this specialisation are equipped to pursue careers in academic research, space agencies, research institutes, or industries requiring advanced skills in data analysis, computational modeling, and scientific programming. They gain the expertise to analyze complex datasets, develop innovative algorithms, and contribute to the rapidly evolving field of data-driven astronomy.