Data Analytics
Niveau
Introduction & in-depth study
Learning outcomes of the courses/module
The participants:
• model energy- and sustainability-related data sets using advanced analytical methods and assess the suitability of different analytical approaches.
• develop simple statistical and algorithmic models for forecasting, classifying or analysing trends in energy data and sustainability indicators.
• validate data and analytical models through quality checks, error analyzes and plausibility checks and interpret the results critically.
• use advanced analytical techniques such as correlation, regression, cluster or time series analyzes to identify more complex relationships.
• derive data-based recommendations for action on energy efficiency, sustainability assessment or operational decisions and argue for them convincingly.
• model energy- and sustainability-related data sets using advanced analytical methods and assess the suitability of different analytical approaches.
• develop simple statistical and algorithmic models for forecasting, classifying or analysing trends in energy data and sustainability indicators.
• validate data and analytical models through quality checks, error analyzes and plausibility checks and interpret the results critically.
• use advanced analytical techniques such as correlation, regression, cluster or time series analyzes to identify more complex relationships.
• derive data-based recommendations for action on energy efficiency, sustainability assessment or operational decisions and argue for them convincingly.
Prerequisites for the course
Digital Tools & Data Skills
Course content
- Advanced analysis methods for energy and sustainability data
- Modeling of basic forecasts and classifications using statistical and algorithmic methods
- Data validation and quality checks, including error and plausibility analyzes
- Fundamentals of time series analysis for consumption and generation data
- Interpretation of analytical models for decision support in energy and sustainability management
- Development of data-based recommendations for action and evaluation of analytical results
- Current methods and tools for advanced data analysis in the energy sector
- Modeling of basic forecasts and classifications using statistical and algorithmic methods
- Data validation and quality checks, including error and plausibility analyzes
- Fundamentals of time series analysis for consumption and generation data
- Interpretation of analytical models for decision support in energy and sustainability management
- Development of data-based recommendations for action and evaluation of analytical results
- Current methods and tools for advanced data analysis in the energy sector
Recommended specialist literature
- Demirbaga, Ü., Aujla, G. S., Jindal, A., & Kalyon, O. (2024). Big Data Analytics: Theory, Techniques, Platforms, and Applications. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-55639-5
- Garg, V., Goel, R., Tiwari, P., & Döngül, E. S. (2024). Handbook of Artificial Intelligence Applications for Industrial Sustainability: Concepts and Practical Examples (1st edn). CRC Press. https://doi.org/10.1201/9781003348351
- Lee, R. (Ed.). (2025). Big Data and Data Science Engineering: Volume 7 (Vol. 759). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-78373-9
- Garg, V., Goel, R., Tiwari, P., & Döngül, E. S. (2024). Handbook of Artificial Intelligence Applications for Industrial Sustainability: Concepts and Practical Examples (1st edn). CRC Press. https://doi.org/10.1201/9781003348351
- Lee, R. (Ed.). (2025). Big Data and Data Science Engineering: Volume 7 (Vol. 759). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-78373-9
Assessment methods and criteria
Portfolio assessment
Language
English
Number of ECTS credits awarded
5
Semester hours per week
Planned teaching and learning method
Lectures, discussions, exercises, case studies, group work
Semester/trimester in which the course/module is offered
5