Course Overview
Data Analytics and Processing in Energy Systems is a practical, application-oriented course introducing modern methods for working with data generated by energy systems.
Course Highlights
A major part of the course is based on wind energy applications and SCADA data from wind turbines.
MAIN GOAL
The course combines fundamental concepts of data analysis with hands-on Python exercises and real engineering datasets.
Learning Outcomes
After completion of this course, you will gain the ability 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.
Meet Your Instructors
Assistant Professor
Admissions
Entry Requirements
- + 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.
- + No advanced Python knowledge is required.
Teaching and Assessment Methods
- + 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.
Application Deadline: TBC.
Practical Notes
- + The course includes guided notebooks and practical examples that allow participants to learn the necessary data-analysis operations step by step.
Fees & Funding
Tuition Fees
Visit institution page for information on fees and application deadlines.
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Course Info
Contact Klaudia Wrzask for any additional information relating to this course.