Upon successful completion, graduates will be able to:

A. Knowledge and conceptual understanding

• Demonstrate systematic and critical knowledge of data science, policy analysis, governance, decision theory and relevant social-science disciplines.
• Explain how data, algorithms, institutions, incentives, laws, political processes and social inequalities interact in policy and organizational decisions.
• Critically evaluate advanced theories and models of evidence-informed policy, public value, risk, uncertainty and collective decision making.
• Demonstrate specialist knowledge of a selected policy domain and understand its institutional, economic, ethical and societal context.

B. Technical and analytical competence

• Design, construct and manage reliable datasets from administrative, survey, transactional, geospatial, textual, sensor, social-media and open-data sources.
• Apply advanced statistical methods, machine-learning techniques, simulation and computational modelling to policy and decision problems.
• Use causal-inference methods to estimate the effects of policies, programs, regulations and interventions.
• Integrate quantitative, qualitative, geospatial and participatory methods within rigorous mixed-methods research designs.
• Assess model performance, uncertainty, robustness, validity, generalizability, fairness and reproducibility.
• Develop interpretable analytical products such as policy models, decision-support systems, dashboards, forecasting tools, risk assessments and evaluation frameworks.

C. Research competence

• Identify significant and researchable problems at the intersection of data science and policy or decision-making.
• Formulate original research questions, hypotheses, conceptual frameworks and methodological strategies.
• Conduct systematic and critical reviews of scholarly, policy and professional literature.
• Design and execute an independent doctoral research project, adapting the design in response to unforeseen methodological, practical or ethical issues.
• Generate original knowledge or advanced applications that make a defensible contribution to data science, policy studies or professional practice.
• Produce research outputs suitable for peer-reviewed publication, policy dissemination or professional implementation.

D. Ethics, governance and responsible innovation

• Apply principles of research integrity, responsible innovation, privacy, data protection, information security and ethical data stewardship.

• Identify and mitigate risks associated with bias, discrimination, opacity, surveillance, exclusion, automation and misuse of data.

• Evaluate the legal, institutional and societal implications of automated and algorithm-supported decision-making.

• Design governance mechanisms that support transparency, explainability, accountability, human oversight and meaningful stakeholder participation.

E. Communication and knowledge exchange

* Communicate complex analytical findings clearly to academic, policy, professional and public audiences.

* Translate research findings into policy briefs, executive recommendations, technical documentation, visualizations and implementation plans.

* Facilitate interdisciplinary collaboration among data scientists, policymakers, domain experts, communities and affected stakeholders.

* Defend methodological choices and research conclusions in seminars, conferences, peer review and a doctoral viva.

1. Professional autonomy and leadership

• Manage an extended research project using appropriate planning, documentation, version control and quality-assurance procedures.
• Exercise independent judgment in situations characterized by incomplete information, uncertainty, competing values and conflicting stakeholder interests.
• Lead or contribute to interdisciplinary research, innovation and policy teams.
• Demonstrate continuing professional development and the ability to identify emerging methods, technologies, risks and policy challenges.