The objective of the BSc Data Science for Global Challenges is to educate students to build bridges between data science and life sciences and thereby contribute to solutions to global challenges. Think about food security, human health, climate change.\n\nThe programme is interdisciplinary and is characterised by the integration of data science knowledge and skills with an understanding of the domains agri-food, health and environment. Graduates of this programme speak both the language of content experts in life sciences and the language of computer scientists and data specialists. This enables them to contribute to solutions to challenges in the life sciences using data science methodologies and, vice versa, to contribute to the further development of data science methodologies based on developments and issues in life sciences.\n\nDuring the BSc Data Science for Global Challenges programme, students develop state-of-art data science knowledge and skills on the one hand, and they learn the key concepts of life sciences on the other hand. To be able to interconnect the domains of agri-food, health and environment with data science they will develop unique skills and the attitude of a bridge builder.\n\nA bridge builder in the context of this bachelor programme is someone with the following characteristics. They are comfortable with speaking both the language of data scientists and life scientists. They possess strong knowledge both in data science and AI quantitative methods and techniques and disciplinary knowledge and skills. The depth of data science and AI and disciplinary knowledge may differ per student, but a minimal level of both data science and domain knowledge defined by the programme learning outcomes must be met. It is important to empathize with the interests and concerns of life scientists in data science and the other way around (i.e. data scientists interests and concerns in life sciences)\n\nFurthermore, a bridge builder promotes 'technology pull' based on requirements coming from the application fields. They understand the potential added value of data-driven solutions in life sciences and are able to convince stakeholders of this. They also think and act interdisciplinarily, integrating perspectives or insights through interaction. The didactical implementation of this profile is explained in the educational principles and methods.