Advanced studies in Data Science, At least 80 cr

Our modern world is changing rapidly. Advances in information-communication technologies, artificial intelligence, and digital platforms alter the way we perceive each other and society. Unprecedented challenges like the climate emergency, geopolitical extremism and the instability of global structures test the limits of our ability to coordinate and find solutions at a societal scale. Within this environment, the availability of large datasets and computing power allow us to approach issues quantitatively and make predictions at a scale never seen before. Using various large and often unstructured datasets to understand complex phenomena requires new methodologies and an interdisciplinary approach, with perspectives from the computer, complexity, natural and social sciences, and tools like data mining, network analysis, mathematical modeling, and statistical inference.

The module Advanced Studies in Data Science is an interdisciplinary major in data-driven research and analytics. Students will learn core and advanced computational, statistical, and mathematical methodologies to collect, analyze, and understand massive datasets of human, technological, and economic interactions. The program will also give an overview of the application areas of these methodologies, as well as current opportunities in confronting real-world problems with a data-driven perspective. At the end of the program, students will be able to use data science to approach complex issues like the interplay between artificial intelligence and socio-economic activity, human behavior in online environments, and many more. Graduates of the program will have the necessary skills to contribute meaningfully to interdisciplinary academic research, join data science teams in the industry, and engage with data-driven roles in the public sector.

The module includes a Master’s thesis, a set of compulsory courses around the core skills of a data science background (machine learning, complexity and network science, statistical modeling, and data-intensive computing), and a set of elective courses on more advanced data-science methodologies and applications of the field.

Advanced Studies in Data Science includes 80 credits:

30 credits Master's thesis

20 credits compulsory courses

30 credits elective courses

After completing the Advanced Studies in Data Science specialization, the student will have knowledge and skills in how to:

• Understand the new possibilities opened by data science for studying relevant, real-world problems in academic research and business tasks.

• Collect large-scale data from diverse sources with tools like data mining, tracking, monitoring, and crawling of data-heavy technological platforms.

• Choose suitable quantitative and data-driven methods for the exploration of complex phenomena in science, society, and the industry from a wide selection of computational and statistical methods, including methodologies for integrating data from different sources during data preprocessing and analysis.

• Explore, model, and predict individual- and system-level patterns in complex, networked, and dynamical phenomena related to society, economics, politics, technology, and nature.

• Use efficient computational and statistical methods to manage and analyze various large and often unstructured datasets. This includes computational algorithms such as efficient parallel computing, optimization approaches, and deep neural networks, and a variety of statistical modeling approaches such as classification, regression, and Bayesian analysis for numerical and text data.

• Implement modern data science tools, including supervised and unsupervised machine learning, online data mining, network analysis and visualization, and natural language processing.

• Identify correlation patterns and causal relations in large-scale datasets, with the goal of building predictive models of socio-technical behavior.

• Communicate with data scientists, decision makers, and the public in general on how data science tools can be used to engage with issues in a quantitative and data-driven way.