Data Science includes fields like machine learning, data mining, deep learning, artificial intelligence, optimization, and statistics, and it relates to terms such as data analytics and big data.
In many areas, incorporating insights from data analysis makes a major difference. Examples of data science in action in our everyday life are product recommendations in online stores or personal assistant systems on smartphones. Many companies want to use data science techniques to optimise their businesses. In the industry, machine learning, optimisation and artificial intelligence are applied in hot developing technologies such as robotics, drones, and self-driving cars.
"Students just come by and ask, there is no barrier, no formality. It’s a friendly, supportive atmosphere. We are here to succeed together, to be a team in learning," says Arthur Zimek, Associate Professor in Computer Science.
In the Data Science group at SDU, statisticians and computer scientists work together for teaching, so we provide expertise in various aspects of our educational programmes as well as a coherent picture. Through close cooperation with other faculties, we are also able to offer courses that connect to the upcoming field of Personalised Medicine, a field which is relevant to everyone and which relies heavily on Data Science.
We are engaged in data science projects with various companies, from small and medium local companies to big players, which is why we can offer hands-on experience in student projects as well as theoretical research at the forefront of this field.
For instance, we are working with the City of Odense and the municipalities of Kolding, Nyborg and Svendborg on improving traffic systems, the planning of public transportation and the allocation of posts for building maintenance in the yearly budgets. In the industrial sector, we have current and past projects on data analysis and optimisation with Danfoss, Ørsted, Energinet, Lego and Aviation Cloud.
The Master's degree programme in Computer science at SDU allows you to choose most of your courses freely according to your personal interests. The following courses are the ones offered within the area of data science in the academic year 2019/2020:
Most of these courses require programming abilities and a basic understanding of linear algebra. You can choose any number of the courses, as long as you meet any prerequisites, and as long as the courses fit into your course of study.
Master Thesis projects
The following are examples of previous Master Thesis topics in the area of Data Science:
- Simulation of Traffic Flow in a Real Urban Network (2018)
- Optimization of coordinated traffic signal intersections (2018)
- Flight Planning in Free Route Airspaces (2017)
- Artificial intelligence in action real-time strategy games (2016)
- Optimizing heat and power production using column generation (2016)
Who teaches Data Science?
Marco Chiarandini is fascinated by the fantastic journey that optimizers undertake to solve timetabling, scheduling and routing problems. The journey moves forward in the abstract world through problem communication, mathematical representation, algorithm design, implementation and experimental analysis. Finally, it returns to the real world with numbers that correspond to actually practicable decisions, that yield systemic improvements and can ultimately ameliorate our lives.
Fernando Colchero is a statistician with particular interest in developing and applying inference methods to understanding population dynamics and demographic trends across the tree of life. He uses Bayesian inference as the statistical framework to explore hidden processes that affect and drive natural populations.
Lene Favrholdt likes to get to the core of an algorithmic problem. She strives to understand the essence of what results can be obtained, looking for precise and intuitive explanations of how and why they can (or cannot) be obtained. She finds communicating this understanding to colleagues and students in a clear and intuitive way very satisfying.
Yuri Goegebeur focuses on theoretical statistics, with main interests in extreme value theory, order statistics and asymptotic results. Although his work is largely theoretical, he has also applied extreme value methods to finance and insurance (claim size modelling, reinsurance), environmental science (ozone pollution and temperature), health science (modelling longevity) and geostatistics (earthquake magnitudes, diamond valuation).
Hans Christian Petersen is a biological anthropologist working in applied statistics, especially focusing on methods and applications related to human, primate and general evolution. Research topics are morphology, multivariate statistics and ways of dealing with datasets with missing values.
Jing Qin is a mathematician whose research areas cover combinatorics, graph algorithms and their applications in RNA Computational Biology. Most recently, she has started working in extreme statistics and applications. She enjoys applying mathematics and statistics to solvin real-life problems, although it sometimes means struggling in between theoretically neat results and not-so-neat practical applications. Well, all struggles lead to better scientific discoveries - eventually.
Richard Röttger is a computer scientist and specialises in various fields of Bioinformatics. In his research he focuses on the analysis of biological networks and large-scale biomedical datasets with the aim of utilizing existing information as efficiently as possible and extracting knowledge from the plethora of available biological data. To that end, he and his group employ state-of-the-art machine learning techniques like deep learning in order to research genes and proteins not in isolation but as a complex choreography of interactions leading to a deeper understanding of organisms and diseases.
Arthur Zimek is not only a computer scientist by training but also studied philosophy, where one of the fundamental questions is: “what can we know?” This question translates into data science by the quest to understand intuition, based on which some machine learning methods work, what assumptions machine learning methods require, and how we can trust and interpret their results.