Curriculum of the Master of Data Science for Public Policy
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.
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.
This course introduces students to the policy process with a governance perspective. It analyses the capacity of political actors to design instruments to influence particular outcomes in different fields. Focusing on governance, rather than governments, students gain a broad perspective about the range of relevant actors involved in the policy process. The course provides students with a thorough understanding of the policy process with the perspective to achieve their own targets as future actors in the policy process.
The course provides an intensive introduction to a wide range of topics, models and theories. It is suitable for students without an economics background, 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, fiscal policy and taxation, game theory and uncertainty, trade, the labour market, and political economy.
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.
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.
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.
This course elucidates 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: foundational legal techniques and sources, such as legal interpretation and argumentation; the relationship between law and policymaking; and the trans-nationalisation of modern law, and the impact of such trans-nationalisation on the law’s form, structures and substance. By examining primary legal materials and their interaction with contemporary public policy issues and dilemmas, students gain competences in reading and applying legal sources, as well as in understanding the impact of law on decision-making at different levels of governance.
Between the first and second years of study, students complete an internship at an institution in the public, private or civil society sector. It is also possible for students to ask for a leave of absence for one or two semesters after their first year of study to gather more substantial professional experience. Students with extensive and relevant experience can be exempt from the internship requirement.
Semester 3 + 4
Building upon the foundational knowledge of the first year of study, students further expand the toolkit they will employ as data scientists and policy practitioners. Students will attend two courses from the Data Science concentration covering methodologies and frameworks that deepen their technical training and build on the methods and skillsets of the first-year courses. They will work through various case studies from different sectors and industries to help appreciate the breadth and scope of the different data science solutions employed in the real-world.
Examples of data science concentration electives:
- Deep learning
- Data science and decision making
In addition to deepening their technical and professional training as data scientists, students will learn about the specific challenges of governing data and AI, of contextualising their growing technical competence, and of navigating these challenges in an organisational context—be this a public institution, a business or an NGO. Students will choose two courses from a range of electives that 1) provide an understanding of the new and complex challenges of governing data and AI, including the ethics and legality of utilising data science in the sphere of public policy; and 2) train the management, leadership and judgement skills necessary to evaluate, address and lead on these challenges in an everyday organisational context.
Examples of Governance and management for data science concentration electives:
- Governance and politics of AI
- AI in government
- Leadership, power and influence
Students deepen their knowledge and expand their policy and data science portfolio by attending two additional portfolio electives. Students will learn to analyse policy-areas and assess the role of data science in tackling societal challenges like inequality; manage processes and transformations in private, public or third sector organisations; and use data science to develop solutions for global governance challenges like international conflict and climate change. Students will also have an opportunity to deepen their competence in particular areas of data science (such as natural language processing). These courses can be selected from the entire catalogue of electives across the School’s graduate programmes.
The master's thesis is the capstone project of the two-year programme. The aim of the module is to enable students to prove their capacity, depth of knowledge and skill level in data science for public policy, by designing, planning and implementing a research project that contributes to developing a solution for a scientific or societal challenge. It is expected that students will make full use of the variety of technical tools, methodologies and academic knowledge that they have acquired over the course of their graduate education to deliver a dissertation in the broader field of data science.
Meet our alumni
Who are our alumni working in the data science field and how did they get to where they are today? We asked three alumni to share their perspectives.