The Data Analytics Lab is part of the University of Geneva, and as such, its main missions is research, teaching and service to the society.
Data analytics can be described as the set of processes allowing decision making based on evidence provided by data. Hence, it includes data processing and organization, as well as data analysis for decision making with controlled decisional risk.
As members of the Data Analytics Lab, we aim at contributing to the developement of new methodologies for data analysis and decision making, that take into account the (Big) data environment, i.e. massively available, less controlled (as compared to experimental settings), possibly continiously updated (dynamically available) and generated by complex underlying processes. The fundamental developments make use of the latest advances in (applied) computer sciences, in particular machine learning. We also aim at making these fundamental developments broadly available (open source) through statistical packages (e.g. R platform) and scientific publications and/or reports in applied statistics.
For better added-value and impact, we aim at collaborating, in an interdisciplinary spirit, with established researchers not only in computer and mathematical sciences, but also in experimental and behavioral sciences, for whom data analysis has become an important and very demanding challenge, in disciplines such as economics, management and business, psychometrics, life sciences (medical and pharmaceutical), population health, engineering (signal processing, navigation) and so on. We also aim at collaborating with (semi-)private institutions that face the challenges of analyzing the data they produce, for the improvement of their products, services as well as for strategic decision making.
Our research activities and network of scientific and private-sector collaborations, are used in our teaching mission. More specifically, we aim at integrating in our core classes, and also new ones, the new data environmment and the challenges they drive for science and society. We also aim at integrating the knowledge of external experts in data analytics within the context of their private/public institution, as for example to provide academic education to students in business or life sciences analytics.