Single-Course Dansk 5 ECTS

Data (Polytechnical Foundation – B.Eng.)

Overall Course Objectives

The course Data introduces students to experimental work and data processing as central elements of the engineer’s toolkit. Although the course focuses on data, the experimental aspect is at its core: students learn to investigate physical problems through measurements, analyze their results, and critically assess the quality of both data and models.

Through planning and conducting small-scale experiments, students gain experience in selecting measurement methods, identifying relevant variables, and controlling influencing factors. They work systematically with measurement uncertainties, including the distinction between random and systematic errors, as well as how uncertainties are quantified and propagated through calculations. At the same time, statistical methods and regression analysis are introduced as tools for describing and evaluating relationships in data.

The course integrates programming in Python as a key tool for handling and analyzing data—from data import and structuring to visualization and modeling. Students learn to compare experimental results with theoretical models, assess the validity of these models, and apply data-driven methods to estimate physical parameters.

Emphasis is placed on developing a critical and reflective approach to experimental work, where students not only acquire technical skills but also gain an understanding of data quality, methodological choices, and the limitations of models. The course thereby supports the ability to work systematically and investigatively with real-world problems in an engineering context.

See course description in Danish

Apply and evaluate measurement uncertainties, including the use of uncertainty propagation and statistical methods for determining uncertainties.
Assess variation in repeated measurements and distinguish between random and systematic uncertainties.
Plan and carry out small-scale experiments, including selecting measurement methods, collecting data, and comparing results with theoretical models.
Design experiments to address specific physical questions, including identifying relevant variables and controlling influencing factors.
Use Python to import, structure, filter, transform, analyze, and visualize data.
Perform regression analysis and evaluate model quality, including residuals and validity range.
Assess agreement between models and data, taking uncertainties into account.
Apply symbolic and numerical methods to solve equations and simple differential equations in the context of data modeling.
Organize, represent, and communicate experimental data in tables, graphs, and short technical reports.
Reflect on data quality, measurement methods, and the limitations of models in experimental investigations.
Apply data-driven methods to estimate physical parameters from experimental measurements.

Introduction to data; generation, collection, filtering, and treatment. Estimating uncertainties and computing with numbers with uncertainties. Data analysis of experimental data. Planning and execution of smaller and larger experiments.

Classroom lectures, tutorials, and experiments.