Course Video
Course Overview
This is an online three weeks program designed to meet the pressing demand for advanced digital skills in the evolving wind energy sector. Delivered as a guided learning journey, the course combines self-paced learning with daily 2 hours live sessions for interaction. Participants will gain expertise in key topics, including research data management, data visualisation, machine learning, and AI applications, all tailored to wind energy and delivered through practical Python programming.
Course Highlights
The course blends theoretical lectures from leading voices in industry and academia - with interactive, hands-on exercises and a capstone project. Participants are also encouraged to bring their own data and projects to tackle practical challenges directly relevant to their work. This approach ensures that participants acquire both conceptual knowledge and immediately applicable skills for real-world wind energy analysis and decision-making.
Key learning areas encompass the full spectrum of digital best practices: data visualisation, metadata management (including industrial FAIR principles, data ontologies, and labelling), basic statistics, exploratory data analysis, data processing, and feature engineering. Participants will also explore machine learning and AI methods, versioning with Git (supported by pre-course video material), as well as licensing and the wider regulatory framework shaping data use in the wind energy sector.
MAIN GOAL
By integrating the latest industry developments and leveraging deep expertise, this lifelong learning module empowers you to upskill rapidly and meet the evolving needs of the wind energy sector.
Learning Outcomes
Upon completion, graduates will be able to:
- + Explain foundational concepts in data science and research data management, and discuss their application within wind energy systems
- + Apply key Python libraries to perform common data science tasks, following best practices for data analysis and visualization in wind energy contexts.
- + Apply machine learning algorithms to address sector-specific challenges in wind energy.
- + Analyse datasets using statistical methods and acritically evaluate the performance of data-driven models.
- + Design and present a capstone project that applies your data science skills to solve real-world wind energy problems.
MEET YOUR INSTRUCTORS
Senior Researcher
Senior Researcher
Senior Scientist
Head of Section
Postdoctoral Researcher
Senior Development Engineer
Senior Researcher
Admissions
Entry Requirements
Background in wind energy engineering, data science, or software development.
Foundational knowledge of Python programming.
Teaching and Assessment Methods
The course is delivered in an online format as a guided learning journey, combining self-paced learning with daily 2 hours live sessions for interaction to increase flexibility while maintaining a strong interactive learning experience.
Self-paced and time-flexible learning components include:
- + Short, focused, recorded video lectures introducing key concepts and methods
- + Curated reading materials and examples tailored to wind energy applications
- + Online quizzes and practical exercises to reinforce learning and support self-assessment
Synchronous live sessions focus on application and interaction. These sessions include:
- + Hands-on coding exercises in Python
- + Guided walkthroughs of real-world wind energy datasets
- + Break-out room activities for peer discussion and collaborative problem-solving
- + Opportunities for live Q&A with teachers
To accommodate a global participant audience, each live session is offered in two time slots covering the same content:
- + First session: 9:00 CEST (serving participants in Asia–Pacific, Europe, and Africa)
- + Second session: 15:00 CEST (serving participants in Europe and the Americas)
All core learning materials are available online throughout the course, enabling participants to revisit content and learn at their own pace and time. Communication and course support are facilitated the learning platform, fostering ongoing dialogue between participants and teachers.
Application Deadline: TBC
Fees & Funding
Tuition Fees
Course Fee: 10,000 DKK
For course-specific questions or if you are looking for a customised training solution for your company, please contact the team at:
