Vorarlberg University
of Applied Sciences

Apply now for your desired degree programme!

Information on individual educational components (ECTS-Course descriptions) per semester

Data Science 2

Course unit title	Data Science 2
Course unit code	072722330401
Language of instruction	German, English
Type of course unit (compulsory, optional)	Compulsory
Teaching hours per week	45
Year of study	2026
Number of ECTS credits allocated	4
Name of lecturer(s)	Elias EDER, Lukas MOOSBRUGGER
courseEvent.detail.semester
Degree programme	Sustainable Energy Systems
Subject area	Engineering Technology
Type of degree	Master part-time
Type of course unit (compulsory, optional)	Compulsory
Course unit code	072722330401
Teaching units	45
Year of study	2026
Name of lecturer(s)	Elias EDER, Lukas MOOSBRUGGER

Requirements and Prerequisites

Course "Data Science 1"

Course content

The course introduces the classical tools of machine learning. The theoretical foundations are explained and the tools are applied to practical examples in a programming environment.

Data preparation and preprocessing for machine learning: samples, features, target, scaling, and data visualizations
Supervised learning: regression vs. classification, k-nearest neighbors, linear and logistic regression, linear support vector machines, (ensembles of) decision trees, introduction to neural networks
Unsupervised learning: clustering, principal component analysis (PCA)
Feature engineering: polynomial features, one-hot encoding, feature selection
Model tuning and evaluation: cross-validation, Bayesian hyperparameter tuning (application-oriented), Monte Carlo analysis, training and test datasets, performance metrics
Time series analysis (autoregressive models and introduction to sequence models)

Learning outcomes

Students acquire foundational knowledge and practical competence in the classical tools of machine learning. Students will be able to:

model relationships in data using various algorithms.
distinguish between supervised and unsupervised learning and are familiar with the classical methods of each category.
understand classical models for regression and classification and interpret their results.
understand the fundamental steps involved in building data-driven models.
understand the influence of dataset selection and model complexity, and know methods for tuning and evaluating their models.
assess the quality and robustness of their model outputs using cross-validation.
apply basic methods for modeling time series data and use simple autoregressive models.

Students develop the following future skills:

Critical Thinking: interpretation of models and results
Digital Literacy: implementation in Python
Communication Skills: communicating analytical methods and results
Environmental and Sustainability Awareness: application to energy data

Planned learning activities and teaching methods

Integrated course with lectures, exercises, coaching and project work.

Assessment methods and criteria

Written intermediate exam (30%)
Oral exam and assessment of project work (65%)
Compulsory project pitch (5%)

For a positive grade, a minimum of 50% of the possible points must be achieved across all parts of the examination.

Comment

None

Internal

Data Science 2