Applied Data Science (MSc)

Data scientists know how to extract knowledge from data. They combine skills in mathematics, computer science, statistics and specific knowledge in an application domain. In Göttingen, we place special emphasis on the interdisciplinary nature of data science. We want to equip you with in-depth knowledge of the key mathematical, statistical and computer science methods for data science, and we also want to make sure that you understand how to apply these methods in an application domain. You can choose from Computational Neuroscience, Bioinformatics, Medical Data Science, Digital Humanities, Computational Sustainability, Digital Business Administration as well as Physical Modelling and Data Analysis. We are currently working on expanding this list and plan to add an application domain in Natural Language Processing. Our Master’s programme is research-oriented, and it should enable you to carry out scientific research projects autonomously. Moreover, we want to teach you how to communicate your insights and reflect on the ethical impacts of collecting and analysing large amounts of data as well as the consequences of automated data-driven decision-making. Beyond research, we also provide the opportunity for internships and interactions with industry partners. The Master's programme in Applied Data Science is a graduate programme that requires 120 ECTS to be successfully completed. The programme includes three areas of study: (1) the core curriculum, (2) the area of professionalisation and (3) the Master’s thesis. The core curriculum covers important data science methods from computer science, mathematics and statistics as well as ethical aspects of data science: Fundamentals of Data Science Statistical methods of Data Science Computer Science methods of Data Science Machine Learning methods In the area of professionalisation, students get many opportunities to study tailored to their individual and subject-specific inclinations as well as their professional ambitions. They choose one application domain, such as Computational Neuroscience, Bioinformatics, Medical Data Science, Digital Humanities, Computational Sustainability, Digital Business Administration, and Physical Modelling and Data Analysis. Furthermore, they carry out a research lab rotation or an industry internship and acquire additional key competencies: Application domain Key competencies Elective courses in Data Science