Data Science Essentials for Wind and Energy Systems

Issued by

DTU Wind and Energy Systems

Awarded to

Sam George Vasanthan

Data Science Essentials for Wind and Energy Systems

This intensive four-day course is designed to equip participants with core data-science and AI/machine-learning skills directly applicable to wind and energy systems. Through a blend of theoretical lectures and hands-on Python-based exercises, where participants learn how to manage, process, analyse and visualise wind-energy data. The curriculum covers research data management, metadata handling (following industrial FAIR and ontology principles), exploratory data analysis, feature engineering, statistical methods, and the application of machine-learning and AI techniques.

Skills proven in this course

Data Science

Wind Energy Analytics

Machine Learning & AI

Data Visualisation & Management

Scientific Computing for Energy

Earning criteria

Attend (virtually or in person) the full four-day program and complete all scheduled lectures and practical sessions.

Complete hands-on Python exercises covering data ingestion, cleaning, visualisation and exploratory data analysis specific to wind-energy datasets.

Demonstrate proficiency in applying one or more machine-learning or AI methods to a wind-energy related problem (e.g. dataset classification, regression, forecasting, feature engineering).

Show understanding of relevant data governance, metadata, licensing or regulatory considerations when handling wind-energy data, demonstrating awareness of responsible data use in the energy domain.

About the issuer

DTU Wind and Energy Systems

wind.dtu.dk/

Earn this certificate of achievement