T6 – Data Science

Algorithmic data analysis and decision making is now commonplace. Be it for online recommender systems, environmental monitoring, simulation and modelling, or smart-city infrastructures, data has become a key ingredient in deploying and optimising large-scale services. While data production is booming – driven by online applications, mobile devices and the Internet of Things – legacy infrastructures like relational databases or numerical computing frameworks are reaching their limits and are being rapidly replaced by a new ecosystem of software and methods for storing, manipulating and analysing steadily growing amounts of data often referred to as Big Data.

This track covers both theoretical foundations as well as practical aspects of dealing with small or large volumes of potentially heterogeneous and noisy data. Core courses belonging to this track cover systems and techniques for data collection, storage, processing, analysis and decision making. Several courses focus on conceptual and architectural issues related to the design and deployment of modern data management infrastructures, with an emphasis on recent systems developed to solve large-scale problems using clusters of commodity machines. Further courses address data analysis, knowledge discovery and decision making from a number of different perspectives, including pattern recognition, online recommendation, machine learning and statistics, including problems in unsupervised, supervised and reinforcement learning. A wide set of applications ranging from targeted advertising, to social network analysis or financial modelling are covered by the offered courses.

Involved research groups