• Develop a basic understanding of numerical algorithms used both professionally and academically in Economics, Business and Finance.
• Be able to utilize the computer to solve problem that humans cannot, and to exploit computational power to gain a deeper understanding of problems for which “pen-and-paper”-solutions are limited/opaque.
• Apply the outlined tools to deepen the knowledge of economic and statistical models concerning demand and supply, monopoly pricing decisions, as well as concepts such as the central limit theorem and endogeneity in estimation.
• Understand how statistical significance can be used to test hypotheses, and analyse the robustness of various estimation techniques.
• Develop a solid understanding of the difference between causality and correlation, and how to separate them using data.

This course teaches you how data and models actually work by letting you see them in action. Instead of memorising rules from a textbook, you use Python to generate your own data, run simple experiments, and test ideas for yourself. You learn what a regression really does (how we draw a line through data to summarise relationships), why averages bounce around from sample to sample (sampling variation), and how we can detect when the numbers mislead us (bias). The course introduces the basic building blocks behind modern data analysis - like simulations, simple linear algebra for estimation, and hands-on data visualisation - in a practical, intuitive way with no black boxes.

At the same time, the course gives you a new way of thinking about evidence. You discover how small mistakes in data can create false patterns (omitted-variable problems), how to check whether a result is trustworthy (bootstrapping and resampling), and why causation is harder than it looks (endogeneity and instrumental variables). By building tools yourself and watching them behave, you develop genuine insight into how economists and data scientists turn raw numbers into knowledge. Students leave the course with stronger intuition, clearer reasoning, and the confidence to take on more advanced analytical work.

The course is based on lectures and exercises in an intertwind way: Concepts are introduced via slides and lectures, and we make that knowledge tangible and permanent through exercises.

The course’s development of personal competencies:

This is a very hands-on and general course that will provide a broad knowledge of the computational work behind data analyses. The methods are not limited to a specific software, but can be applied in a multitude of context. Thus, students can expect to leave the course with a transferable newfound knowledge of the digital revolution and how its associated tools can be used in an economic context. You will understand how to formulate a problem, how to use the computer to analyse it, and ultimately to solve it.

• Classic and basic theory
• New theory
• Methodology

• Analysis
• Activities that contribute to new or existing research projects
• Students conduct independent research-like activities under supervision

Textbook/ selected chapters will be provided