Course info

The objective of the course is to equip students with the essential IT knowledge and skills required to facilitate the development and implementation of technological solutions with a focus on relevant problems in the energy sector. This objective will be achieved by leveraging statistical and machine learning tools from the domain of data science.

The course is intended for students who have relevant knowledge for working with data. The course follows a blended learning methodology, incorporating a combination of lectures and practical assignments. During the lectures, the course covers the theoretical and algorithmic foundations of data science. Data from the energy sector is utilized to demonstrate the theory, with implementation primarily using the Python programming language.
The purpose of practical sessions is to offer students examples of problem-solving to complement the theoretical concepts and to help in addressing particular issues or challenges they might face.
The course encompasses fundamental data science concepts such as regression, classification, profiling, and time series analysis. Students will learn data processing and visualization principles, along with their software implementation. The course further addresses relevant energy-related problems and introduces appropriate techniques with a focus on technological solutions.
Upon course completion, students will possess a comprehensive understanding of the methods and techniques necessary to address significant challenges in the energy sector. Additionally, they will have developed both individual and collaborative work competencies.

Upon completion of the course, the student:
- transforms challenges in the energy sector into standard data science problems;
- chooses, combines, and applies appropriate methods for data processing, modelling, and analysis;
- examines and visualises the data;
- applies appropriate metrics and presents data in technical reports to present and justify the findings.