Denmark

This course is structured as a research project in which samples are collected, investigated using various analytical methods, processed using statistical methods, and compared to other studies. The focus lies on abrupt climate changes and the role of ocean circulation during the last ~50 thousand years in the North Atlantic region. The course takes a hands-on approach, using marine sedimentary archives as the basis for reconstructing past climate and environmental conditions. Through weekly theoretical lectures accompanied by extensive practical work in groups (field excursion, laboratory analyses, presentations and data processing), the subject progresses and culminates in an individual written report as course exam.

This course conveys insight into the cognitive mechanisms and representations underpinning human meaning-making. The course presents an overview of the cognitive processes and mechanisms involved in human meaning-making, including conceptual metaphor theory, image schemas, framing, and blending. The theories employed stem from cognitive science, cognitive psychology, cognitive linguistics, and semiotics. The course also provides insight into the relationship between meaning-making and perception, as well as sensory-motor experience. Additionally, it seeks to give students a general understanding of what meaning is, regardless of whether it is expressed in language, images, or speech, or manifests itself through perception. Finally, this course provides the general theoretical tools required to analyze specific cognitive and cultural phenomena. The course is adapted to accommodate the diverse backgrounds of the students.

This course considers global business strategy from two perspectives: the first focuses on the internationalization process of the firm that covers decisions needed to be made prior to the entry into a foreign market (pre-entry), while the second covers issues that may emerge or need to be taken into account after a firm’s entry into a foreign market, such as changing government regulations or demands of local stakeholders (post-entry). Offering a global perspective on firm strategy, this innovative course is a blend between international business and strategy with the elements of political science, sociology, and marketing. When dealing with each topic, it looks at theories and models that lie at the foundation of our understanding of their properties and basic relationships with firm strategy and its outcomes, and then look at them in a global perspective, i.e. how they may be different across countries and organizations. The course entails several case-based workshops, group reports, and presentations, all of which give students a chance to generate hands-on experience with the theories and models of the course.

This course examines the psychological, physiological, and sociological foundations of music across the lifespan. It focuses on a broad range of topics including, among others, what happens in the brain when we play or are exposed to music, and what the clinical implications of music are. Students are encouraged to apply the acquired theoretical knowledge to develop new scientific research hypotheses and ideas related to the field.

This course explores texts for children and young adults in a historical and contemporary perspective. Students are introduced to children’s literature studies as an interdisciplinary field, related to theory, concepts, and analytical strategies from comparative literature, childhood studies, book history, and media studies. It analyzes works by canonical authors such as Astrid Lindgren, Hans Christian Andersen, and Lewis Caroll, as well as contemporary texts and media, and relates them to transnational and transmedial interpretations and adaptations. The relationship between children’s literature and changing concepts of childhood and youth is discussed, also in relation to different aspects of identity formation in local and global contexts.

This course includes a theoretical and practical introduction to the reflection seismic and acoustic method and to interpretation of geophysical well logs and geotechnical measurements. Teaching consists of integrated lectures and exercises and a one-day fieldtrip with the research vessel Aurora. Lectures include: acquisition and processing of reflection seismic data and other acoustic data; the physical principles of geophysical well logs and geotechnical measurements; the workflow in an integrated interpretation. The theoretical introduction is succeeded by interpretation of seismic data and qualitative and quantitative interpretation of well logs and geotechnical measurements and how such data are used for analyzing the geological structure and development of both shallow and deep sediments with focus on the industrial applications and the green transition.

This course introduces psychological barriers to sustainable behavior, discussing, for instance, the role of social influence, attitudes and beliefs, or situational factors. The last weeks are dedicated to project work in which students use the acquired knowledge to examine specific behaviors and develop practical implications to foster sustainability in everyday life.

This course provides methodological tools and applies them to case-based analysis of real-world problems. This training equips students with practical skills and critical thinking useful for future careers such as consulting in applied economics or other related professions in industry requiring data analysis. Successful completion of the course provides students with a comprehensive understanding of the process involved with a case-based investigation in business economics. This includes defining the problem at the start of a case, identifying potential solutions, applying empirical tools and statistical methods to conduct quantitative data analysis, as well as presenting the results. The course emphasizes data-driven analysis, but students are also expected to base this on economic principles and theoretical foundations. Statistical software (for example, Excel, R) are used for data analysis. The course consists of a mix of lectures and guided project work that culminates in a written assignment. The assignment is completed small groups, and students also present the content of their assignment at the end of the course.

This course provides an introduction to the application of various forecasting techniques. It introduces quantitative methods and techniques for time series modeling, analysis, and forecasting. Emphasis is also put on the applications in economic and business related areas. Computing is an integral part of this course, therefore all students are expected to do data analysis, modeling, and forecasting with computer programming software. The focus of the course is to use past data to predict the future. The key concept is that there is an underlying process that gives rise to the data. Using statistical properties of that process, we can develop forecasts. Forecasting methodology is presented in a lecture format in the first part of each class meeting. The application, however, is the centerpiece of the presentation. In the second part of the class meeting, students work on in-class applications. The course starts start with simple models that are widely used in business and progresses to modern methods that are used by professional forecasters and economists. It studies basic components of time-series data, such as trend, seasonal, and cyclical components, as well as basic model specification techniques, such as moving average and auto regressions, used in the forecasting of time-series. All forecasting methods are illustrated with detailed real world applications designed to mimic typical forecasting situations. The course uses applications not simply to illustrate the methods but also drive home an important lesson, the limitations of forecasting, by presenting truly realistic examples in which not everything works perfectly. Examples of the applications include, but are not limited to, forecasting retail sales, hotel occupancy, fishery output, consumer loan requests, predicting expansion of fast food chain stores, modeling and forecasting macroeconomic activity indices such as Gross Domestic Product, unemployment and inflation, modeling development of a small open economy, forecasting New York Stock Exchange index and currency exchange rates and many other applications.

This course introduces fundamental concepts of econometrics and data analysis that form the basis for data driven decision making, empirical analysis of causal relationships, and forecasting. It covers matrices and their use in linear regression analysis, probability distributions and their role in carrying out valid data approximations, and estimation methods and their importance in producing credible results of any data analysis. The course also introduces programming with R, which is the main programming language of statistical computing. It starts out with basic R operations and then, with time, students learn about ways to write their own functions in R.