Økonomi og ledelse

Quantitative decision-making builds on a foundation of mathematics, statistics, and programming. In this course, students will be introduced to all three of these fields in the context of economic modelling and data analysis. The organizing theme for the course is the idea of optimization. Optimization is a governing principle in the analysis of decision-making and is at the heart of economic model building. Optimization is also at the heart of data analysis, with applications that range from curve-fitting to data categorization. Concretely, the course will cover topics related to linear algebra, optimization, parametric inference, and algorithms. Moreover, the course will teach programming techniques with special emphasis on applications to economic modelling and data analysis. At the conclusion of the course, students will be able to understand and work with modern techniques in quantitative decision-making.

The purpose of this course is to provide economics and management students with hands-on insight into how modern software systems are designed, implemented, and maintained. The course bridges the gap between business understanding and software development by giving students the ability to both communicate effectively with developers and create simple, functional prototypes.

Students are introduced to the principles of object-oriented programming and agile project methods, learning how software design, testing, and refactoring contribute to quality and efficiency. Emphasis is placed on developing a critical understanding enabling future managers to assess technical arguments and solutions in collaboration with professional developers.

This course is intended as a master level introduction to research methodology and the research process. This introduction gives the students an overview of the basic concept, methods, and practice of research.

Research is a cyclical process where new and carefully planned investigations build and extend on established work. The aim is to provide students with a fundamental understanding of research as a conceptual, empirical, and practical approach to gathering new insight and knowledge. The content provides a broad overview of how researchers work within the fields of economy, innovation, and technology. It presents students with relevant methods from these domains, along with their possibilities and limitations.

Students will learn a systematic approach to empirical investigation, including literature search, research design and methodology, qualitative and quantitative analyses, and the presentation and evaluation of results.

At completion of the course, students will be able to study and interpret existing research on a topic and suggest approaches to broaden or deepen knowledge within a given topic.

Cybersecurity is an inseparable part of our digitalized life, including business operations in a modern society. In this course, students will be introduced to fundamental concepts from cyber security and various needs that businesses have with respect to digitalization and keeping the data safe and protected. Special emphasis is placed on general principles within cybersecurity, existing forms of attack, protection mechanisms and known incidents. Students will also learn about risk, access control, cryptography, cloud security, digital forensics and incident response. Furthermore, the students will gain knowledge about how privacy, GDPR and corresponding legislation works, and the consequences of these regulations on organizations when it comes to establishing a holistic approach on all levels: Executive Level, Security Operations, Process Management. Finally, the course will focus on understanding the security culture, software development and outsourcing in the Internet of Things and Operational Technologies.

This course offers a graduate-level examination of the theory of industrial organization. Industrial organization examines how firms interact in markets and provides the analytical foundation for both competitive strategy and competition policy.

In this course, students learn theory and methods for analyzing market behavior, evaluating regulatory policies, and designing strategic responses. The course has four main themes: (i) Competition policy and antitrust, (ii) market structure, (iii) strategic competition and pricing, and (iv) new business models and market mechanisms, including platforms and networks.

During the course, students will investigate contemporary debates in industrial organization related to issues such as pricing, concentration, contestability, and digital competition. This will prepare them to succeed as managers and policy-makers. Open questions identified in the course will also be a fruitful starting point for master theses.

Markets and organizations are both mechanisms for coordinating economic activity, yet they operate under different principles and constraints. This course explores the economic foundations of why organizations exist, how they are structured, and how they function as arenas for cooperation and competition.

Through advanced analytical tools and economic reasoning, students will examine the strategic behavior of agents within organizations—across both bilateral and multilateral settings. The course also encourages students to identify open questions in the literature and translate these into potential topics for their master’s thesis.

Modern organizations are data intensive. However, not all organizations are able to take advantage of their data resources. What distinguishes successful organizations is that they deliberately leverage the collection and analysis of data to improve decision-making.

In this course, students will be introduced to a wide range of data analysis techniques. The first part of the course covers data description, including multivariate regression and linear model fitting. The second part of the course focuses on causal analysis, with applications that include experiments, regression analysis, and quasi-experiments. The third part of the course introduces topics from applied data science, including basic machine learning. Throughout all three parts of the course, there is an emphasis on the valid application and interpretation of techniques.

Students are expected to work with data using programming techniques during both lectures and on their own. The goal is to cultivate mature data users who know how to carry out data collection, when and how to use specific techniques, the scope and limitations of their analyses, and how to communicate results in the context of organizational decision-making. At the conclusion of the course, participants will be expected to conduct a preliminary study in which they apply data analysis to a question of their own choosing.

Macroeconomics studies the behavior and performance of an economy, focusing on aggregate variables like GDP, inflation, unemployment, and economic growth. It combines theoretical frameworks with empirical analysis to address both short-term fluctuations and long-term trends.

This course covers the key building blocks of modern macroeconomic theory and policy. Students will explore the dynamic interplay between short-term economic fluctuations, policy responses, and long-term growth. By bridging theoretical foundations with applied insights, the course equips students with the analytical tools and empirical techniques to assess macroeconomic developments and policy impacts.

The course is structured around three primary themes:

1. Business Cycles: Understanding economic fluctuations, their causes, and their implications.
2. Monetary and Fiscal Policy: Evaluating the role of policy in stabilizing the economy and influencing long-term outcomes.
3. Long-Term Economic Growth: Analyzing the drivers of sustained growth and the trade-offs involved in addressing long-term challenges.