Psychometrics
VAO
1 Mar 2027 – 3 May 2027
Course Schedule 2026–2027
The Excel calendar is presented as an interactive infographic by phase, school, and day-time. Week counts are actual class sessions after official holidays are excluded.
Stage 3 · 1 Mar – 4 Jun 2027 · Term break: 23 Jan – 28 Feb 2027
Psychometrics
VAO
1 Mar 2027 – 3 May 2027
Climate Change and Global Policies
YDSO
1 Mar 2027 – 3 May 2027
Panel Data
VAO
2 Mar 2027 – 11 May 2027
Corporate Sustainability
YDSO
2 Mar 2027 – 11 May 2027
Data Analytics — 3. Models and Optimisation
VAO
3 Mar 2027 – 14 Nis 2027
Data Analytics — 4. Tree-Based Algorithms and End-to-End Project
VAO
21 Nis 2027 – 26 May 2027
Environmental Sustainability
YDSO
3 Mar 2027 – 12 May 2027
Digital Humanities
VAO
4 Mar 2027 – 13 May 2027
Circular Economy and the Sustainable Finance Ecosystem
YDSO
4 Mar 2027 – 13 May 2027
Computational Social Science
VAO
5 Mar 2027 – 4 Haz 2027
Social Sustainability
YDSO
5 Mar 2027 – 30 Nis 2027
Biostatistics
VAO
1 Mar 2027 – 10 May 2027
Clean Energy Technologies
YDSO
1 Mar 2027 – 10 May 2027
Fundamentals of Statistics
VAO
2 Mar 2027 – 11 May 2027
Modern Software Development
YZO
3 Mar 2027 – 2 Haz 2027
Artificial Intelligence: Enabling Tools
YZO
4 Mar 2027 – 13 May 2027
Artificial Intelligence: Machine Learning
YZO
5 Mar 2027 – 4 Haz 2027
Monday · 10.00–13.00
Module course
Practice-oriented training in measurement and data analysis, from scale development to SEM, Rasch, and Item Response Theory.
Monday · 10.00–13.00
Module course
A module that jointly addresses the scientific foundations of climate change together with mitigation, adaptation, and global policy instruments.
Monday · 14.00–17.00
Module course
A 10-week practice-focused biostatistics programme for health research, building analytical competence through clinical and epidemiological scenarios.
Monday · 14.00–17.00
Module course
A practice-oriented module covering hydrogen, energy storage, solar and wind, and clean combustion technologies.
Tuesday · 10.00–13.00
Module course
Analyse social and economic panel data with R and apply econometric methods to real datasets.
Tuesday · 10.00–13.00
Module course
A module on how sustainability connects to corporate strategy, reporting, and finance.
Tuesday · 14.00–17.00
Module course
A foundational module spanning data literacy to statistical analysis with Excel, SPSS, and R—focused on choosing methods and interpreting findings.
Wednesday · 10.00–13.00
Data Analytics module
An end-to-end analytics pipeline with R: basic statistics, visualisation, machine learning, and explainable AI.
Wednesday · 10.00–13.00
Data Analytics module
An end-to-end analytics pipeline with R: basic statistics, visualisation, machine learning, and explainable AI.
Wednesday · 10.00–13.00
Module course
A module addressing environmental sustainability through natural resource management, footprint accounting, and the circular economy.
Wednesday · 14.00–17.00
Module course
An applied pathway for designing, validating, and turning an idea into a sustainable software product in the age of artificial intelligence.
Thursday · 10.00–13.00
Module course
An applied introduction connecting humanities research questions with digital methods, data analysis, and computational approaches.
Thursday · 10.00–13.00
Module course
A module examining the relationships among the circular economy, sustainable finance, climate economics, and just transition.
Thursday · 14.00–17.00
Module course
Learn how to integrate ChatGPT, Claude, and other digital research tools into academic workflows in an ethical and practical way.
Friday · 10.00–13.00
Module course
An interdisciplinary module linking digital traces to social science questions through text, network, and spatial analysis (Fridays 10:00–13:00, 13 weeks).
Friday · 10.00–13.00
Module course
A module addressing the social dimension of sustainability through equality, inclusion, stakeholder engagement, and ethical decision-making.
Friday · 14.00–17.00
Module course
Understand how artificial intelligence works through machine learning, neural networks, deep learning, and reinforcement learning—and build a solid foundation for your own projects.