Applied Data Analysis for Interdisciplinary Environmental Studies

The course introduces students to applied data analysis through a progressive structure that combines conceptual understanding, practical skills, and interdisciplinary application. The first part of the course covers an introduction to data literacy in environmental and social studies, including an overview of central Swedish data providers, such as Statistics Sweden (SCB), the Swedish Civil Defence and Resilience Agency (MCF), the Swedish Meteorological and Hydrological Institute (SMHI), and the Swedish mapping, cadastral, and land registration authority, Lantmäteriet. Ethical considerations, uncertainty, and responsibility in the use and interpretation of data are discussed with the aim of establishing a critical foundation for working with real-world datasets. In the next step, the course focuses on basic data types and data preparation, including tabular data and data that varies in time and space. Students develop basic skills in cleaning and structuring data while working with common challenges related to data quality, bias, and limitations. The course also addresses principles for clear and ethical data visualisation. Students learn how to work with charts, maps, and infographics, with a particular focus on visual storytelling and on communicating uncertainty. In the latter part of the course, concepts such as environmental risk, exposure, and vulnerability are introduced, which are applied through interdisciplinary case studies linked to natural events, such as floods or heat waves. This allows students to analyse real-world environmental challenges using data-driven approaches. In the final part of the course, students integrate environmental and societal data perspectives by addressing issues of scale, uncertainty, and interdisciplinary interpretation. The course concludes with an individually applied project where students translate their analyses into insights that are relevant for societal decision-making. The course consists of several components with individual assignments and ends with a final project that brings the components together.

Progressive specialisation: G1N (has only upper‐secondary level entry requirements)

Education level: Undergraduate level

Admission requirements: General entry requirements plus upper secondary level English 6 or English level 2.

Selection: Selection is usually based on your grade point average from upper secondary school or the number of credit points from previous university studies, or both.