Master of Data Science for Public Policy

​​​​​​This course introduces students to the policy process from a governance perspective. The course analyses political actors` capacity to design instruments in order to influence particular outcomes in different policy fields. Focusing on governance rather than governments, students gain a broad understanding of the relevant actors involved in the policy process. Students will also acquire an in-depth understanding of the policy instruments, implementation and evaluation, and of how to influence agendas, outcomes and reforms as future policy analysts and actors in the policy process. This course is taught as a seminar and offered with different policy focuses. Please note that available Policy Process courses vary by semester.

For examples of Policy Process courses offered in recent semesters, see the Course Catalogue.

Economics I provides an intensive introduction to a wide range of topics, models and theories. It is suitable for students without a background in economics, but students with prior training will also benefit. The course focuses on core topics in economics, such as supply and demand analysis, the role of markets and prices, welfare analysis, competition and monopolistic pricing, asymmetric information, externalities and government intervention, game theory and uncertainty, trade, the labour market, and political economy. The course is taught as a lecture and complemented by a lab, in which students have the chance to discuss and further elaborate on the topics examined in the lecture. Students with a strong economics background can apply for a waiver.

You can find more information in the course catalogue:

• Economics I Intro
• Advanced economics: Concepts and policy applications
  (For students with a background in Economics, who are granted a waiver)

Machine learning is a core technology of artificial intelligence and data science that enables computers to act without being explicitly programmed. Recent advances in machine learning have given us, inter alia, self-driving cars, AlphaGo, Amazon, and Netflix. This technology has also allowed us to predict armed conflict and post-electoral violence, detect fake news, develop targeted provision of care and public services, and implement early policy interventions. This course provides a hands-on introduction to machine learning. By the end of this course students will have a sound understanding of the key concepts of machine learning, the ability to analyse data using some of its main methods, and a solid foundation for more advanced or more specialised study.

For more information, see the Course Catalogue. 

This course equips students with conceptual knowledge of the data science project lifecycle and enables them to apply it to practice with statistical software. By the end of the course, students will have solidified their coding workflow, understand different types of data usage and database management, and be able to begin experimenting, collecting and wrangling data for analysis and research.

For more information, see the Course Catalogue.

Mathematics is foundational to data science. This course aims to deliver compact and tailored introduction to the core mathematical concepts of data science. Upon completing the course, students should have a broad understanding of linear algebra, probability and statistics, and optimisation necessary for data science.

For more information, see the Course Catalogue.

Public policy and data-driven organisations are complex arenas, and the ability to uncover causal connections rather than simple correlations is vital in evidence-based decision making. This course teaches the analytical framework of contemporary causal inference, connected to modern statistical methods and machine learning. By the end of the course, students will be able to understand, analyse and suggest improvements for statistical techniques and causal identification strategies in existing research, and will begin conducting their own independent causal analysis, forming hypotheses and testing their validity.

For more information, see the Course Catalogue.

If programming is an art, then data structures and algorithms are the colours and strokes with which programmers display their expertise. Data structures are the manner in which data is stored so that algorithms can operate on them. Algorithms, in turn, are instructions on how to handle data efficiently to achieve a desired result. By the end of the course, students will understand these elementary building blocks of programming, solve simple coding problems, evaluate the complexity and efficiency of algorithms, and know the best use cases for each type of data structure.

For more information, see the Course Catalogue.

​​​​​​This course aims to elucidate the relationship in modern societies between law and governance, i.e. between legal structures and rules, and decision-making. It is divided into three main parts: 1) foundational legal techniques and sources, such as legal interpretation and argumentation; 2) the relationship between law and policy-making (i.e. the use of law both as a constraint upon and as a vehicle for public policy); and 3) the transnationalisation of modern law, and the impact of such transnationalisation on the law’s form, structures and substance. This course is taught as a seminar.

For more information, see the Course Catalogue.

Students attend two electives covering data science methodologies and frameworks that deepen their technical training and build on the methods and skillsets of the first-year courses. Students will also work through case studies from various sectors and industries to appreciate and learn the breadth and scope of the different data science solutions employed in the real-world, and they will learn to build conceptual frameworks to turn data into actionable insights.

Please note that available electives vary by semester. For examples of electives that have been on offer in recent semesters, see the Course Catalogue.

Students will take two electives from a catalogue labelled “Governance and Management for Data Science”. These courses will equip students with the empirical knowledge, conceptual awareness and analytical tools to be able to grasp and assess 1) the governance of data and AI, including questions of law, ethics and politics; and 2) the management and leadership skills required to evaluate and address these challenges in an organisational context.

Please note that available electives vary by semester. For examples of electives that have been on offer in recent semesters, see the Course Catalogue.

In their second year of studies, MDS students deepen their training in data science, become systematically exposed to the challenge of governing and managing data driven processes and organisations, and choose to either further specialise in data science, or to broaden their understanding to a different field of public policy, (global) governance or analytical tools. Portfolio electives can be selected from the entire catalogue of electives, provided that spots are still available in the requested course.

Courses are offered in a variety of fields, including:

• Data science
• Digitalisation and digital governance
• Economics and economic policies
• European affairs
• International affairs
• Normative foundations of (global) governance
• Public management and organisation
• Sustainability
• Tools of policy analysis
• Welfare

Please note that available elective courses vary by semester. For examples of electives that have been on offer in recent semesters, see the Course Catalogue.