COURSE CURRICULUM

The Data Science: Computer Science master’s specialisation at Leiden University is a research-oriented program designed to provide students with advanced expertise in data analysis, machine learning, and computational methods. Situated at the interface of computer science and statistical science, the program equips students to extract meaningful insights from complex and large-scale datasets, addressing challenges across scientific, technological, and societal domains. The curriculum emphasizes both theoretical foundations and practical skills, ensuring that graduates are capable of designing, implementing, and evaluating data-driven solutions.The program consists of three main components: a core program (36 EC), specialisation courses and seminars (42 EC), and a master’s thesis research project (42 EC). The core program covers essential topics in algorithms, data structures, statistics, and probability theory, while the specialisation courses focus on advanced areas such as data mining, deep learning, network analysis, and text analytics. Students also acquire proficiency in programming, particularly in Python, and develop a solid understanding of artificial intelligence and machine learning methods. The master’s thesis project allows students to undertake an original research question under expert supervision, giving them the opportunity to apply computational and statistical techniques, perform independent analyses, and present their findings in a scientific context.The program requires prior knowledge in algorithms, complexity theory, programming, and statistics, as well as strong academic reading and writing skills and proficiency in English. Educational methods include computer labs, lectures, working groups, individual research papers, assignments, presentations, and literature studies, all designed to enhance critical thinking, problem-solving, and communication skills.Graduates of this program emerge as highly skilled data scientists, capable of conducting independent research, developing data-driven solutions, and contributing to innovation in academia, industry, and public sectors. They are well-prepared to pursue careers in data science, artificial intelligence, analytics, and further academic studies, combining computational expertise with analytical and research capabilities.