Data Analytics and Processing in Energy Systems is a practical, application-oriented course introducing modern methods for working with data generated by energy systems. The course combines fundamental concepts of data analysis with hands-on Python exercises and real engineering datasets.
A major part of the course is based on wind energy applications and SCADA data from wind turbines. Participants learn how to explore large datasets, assess data quality, calculate descriptive statistics, analyze distributions and variability, identify operating conditions, and create meaningful visualizations. Particular attention is given to interpreting minimum, maximum, mean, range and standard deviation values, which provide important information about both turbine operation and data quality.
The course also introduces data preprocessing, relationships between variables, operational and environmental effects, and selected approaches to anomaly detection and condition monitoring. Python notebooks allow participants to apply the presented methods directly to real-world data and develop their own analytical workflow.
The course is delivered entirely online and asynchronously in English. It corresponds to approximately 100 hours of learning (4 ECTS), including video lectures, tutorials, and prepared materials for guided self-study. Participants can organize their learning independently and access the course materials for ten weeks, making the program suitable for both students and working professionals interested in data-driven analysis of energy systems.
No advanced programming or data science experience is required.
Participants should have a basic understanding of engineering, energy systems, physics or a related technical field. Basic familiarity with Python is helpful but not essential, as the course provides practical examples and guided Jupyter/Google Colab notebooks.
Participants need a computer with internet access and a modern web browser. All practical exercises can be completed using freely available Python tools.
Klaudia Wrzask, PhD, Eng.
Course author and instructor specializing in data analysis, modeling and diagnostics of engineering systems, with particular experience in wind turbine monitoring and SCADA data analysis. Her research and teaching combine engineering knowledge with practical applications of Python, statistical methods, and data-driven modeling in energy systems.
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Do I need previous Python experience?
No advanced Python knowledge is required. The course includes guided notebooks and practical examples that allow participants to learn the necessary data-analysis operations step by step.
No. The practical exercises can be completed in Google Colab or another compatible Jupyter environment.
The course uses real engineering datasets, with particular emphasis on wind turbine SCADA data and operational and environmental measurements.
Yes. The course is fully asynchronous. Participants can organize their learning indepedently and access all learning materials for 10 weeks.
The total estimated workload is approximately 100 hours, equivalent to 4 ECTS, including lectures, tutorials and prepared self-study activities.
Yes. The course combines explanatory materials with Python notebooks, visualisations, data analysis exercises, quizzes, and independent tasks.
