Data Analytics & Modeling (E)
Niveau
Advanced study
Learning outcomes of the courses/module
The participants:
• analyze large, heterogeneous data volumes from energy & sustainability management contexts and identify relevant patterns, anomalies and relationships.
• assess big data architectures as well as data quality and data integration processes with regard to their suitability for strategic decisions in energy & sustainability management.
• model forecasts, clusters or scenarios using advanced analytical methods and design data-based decision models for complex questions.
• reflect on ethical, data protection and organizational challenges of data-driven decision-making processes and develop guidelines for the responsible use of big data.
• analyze large, heterogeneous data volumes from energy & sustainability management contexts and identify relevant patterns, anomalies and relationships.
• assess big data architectures as well as data quality and data integration processes with regard to their suitability for strategic decisions in energy & sustainability management.
• model forecasts, clusters or scenarios using advanced analytical methods and design data-based decision models for complex questions.
• reflect on ethical, data protection and organizational challenges of data-driven decision-making processes and develop guidelines for the responsible use of big data.
Prerequisites for the course
Fundamentals of statistics & data management from the bachelor's program; basic knowledge of digital tools
Course content
- Big data fundamentals in energy & sustainability management
- Data quality, data governance, ETL processes and data pipelines
- Exploratory data analysis & feature engineering for large data volumes
- Modeling methods
- Big data tools & ecosystems
- Data quality, data governance, ETL processes and data pipelines
- Exploratory data analysis & feature engineering for large data volumes
- Modeling methods
- Big data tools & ecosystems
Recommended specialist literature
- De Wolf, C., Çetin, S., & Bocken, N. M. P. (Eds). (2024). A Circular Built Environment in the Digital Age. Springer International Publishing. https://doi.org/10.1007/978-3-031-39675-5
- 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
- Heath, C., & Starr, K. (2022). Making numbers count: The art and science of communicating numbers (First Avid Reader Press hardcover edition). Avid Reader Press.
- Viceconti, M., & Emili, L. (Eds). (2024). Toward Good Simulation Practice: Best Practices for the Use of Computational Modeling and Simulation in the Regulatory Process of Biomedical Products. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-48284-7
- 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
- Heath, C., & Starr, K. (2022). Making numbers count: The art and science of communicating numbers (First Avid Reader Press hardcover edition). Avid Reader Press.
- Viceconti, M., & Emili, L. (Eds). (2024). Toward Good Simulation Practice: Best Practices for the Use of Computational Modeling and Simulation in the Regulatory Process of Biomedical Products. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-48284-7
Assessment methods and criteria
Portfolio examination
Language
English
Number of ECTS credits awarded
4
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
2