Energy 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.

NB! This course will take place in autumn semester 2024/2025 which starts on 2nd of September and ends 26th of January (you can find that information under Start date section). The real course start and end dates will be announced at the beginning of September at the latest.

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.

Final assessment can consist of one test/assignment or several smaller assignments completed during the whole course. After declaring a course the student can re-sit the exam/assessment once. Assessment can be graded or non-graded. For specific information about the assessment process please get in touch with the contact person of this course. For specific information about grade transfer please contact your home university

• - Applied data science with Python and Jupyter: Use powerful industry-standard tools to unlock new, actionable insights from your data, A. Galea, 2018, Packt Publishing
• - An introduction to statistical learning with applications in R, G. James, D. Witten, T. Hastie, R. Tibshirani, 2nd ed., 2021
• - The elements of statistical learning: Data mining, Inference, and Prediction, J. Friedman, T. Hastie, R. Tibshirani, 2nd ed., 2008
• - Energy Informatics, R. Watson, M.-C. Boudreau, Kindle edition, 2011

lectures, practices