Data Analytics for Sustainability

Please note: the information in the course manual is binding.


In this course you pick one of two tracks: the energy system modelling track with Python, or the Data Science track with R.

After completing the course, students can:

• Translate sustainability issues into a data-oriented question and identify the correct methods and tools to treat the question.
• Understand and explain different data analysis methods and tools.
• In the ESM track you will learn:
  • The basics of programming in python
  • How to model energy systems in Python
• In the Data Science track you will learn
  • The basics of programming in R
  • How to analyse textual data using semantic models and topic modelling
  • How to analyse emotions and sentiment in textual data (think of how people feel about technologies, policies, actions)
  • How to use large language models in data science, locally.
  • How to extract entities from textual data (locations, people, companies, biological entities)
  • Create and analyse geographical maps
  • Perform geographical analysis of the data

The aim of this course is to equip students with cutting-edge programming methods and tools for analyzing various data types commonly encountered in sustainability studies. Students will  learn how to use open-source programming tools, R and Python, to process, visualize, and analyze data from multiple sources.

The course offers two tracks focused on specific areas: Energy System Modelling and Geospatial & Textual data analysis. Each module introduces advanced, domain-specific data analysis techniques.

The course includes a mandatory programming module to learn the basics of programming and prepares students for the different modules they can choose. This choice allows students to tailor their learning and develop a personalized portfolio of data analysis skills.