Data Science: Creating value through data
Data Science is about extracting knowledge from digital data. Given the ubiquitous availability of digital data, Data Science has a wide range of applications, from supporting scientific discoveries in the life sciences to understanding the mechanisms through which disinformation spreads in social media.
Overview
Data Science is about extracting knowledge from digital data. Given the ubiquitous availability of digital data, Data Science has a wide range of applications, from supporting scientific discoveries in the life sciences to understanding the mechanisms through which disinformation spreads in social media.
The volume, variety, and velocity often characterising contemporary digital data requires the application and development of methods rooted in multiple disciplines, such as databases, distributed computing, machine learning, data mining, and visualisation. This makes Data Science a multi-disciplinary field. For example, the processing of very large data requires efficient data management, leading to the frequent application of methods from databases, artificial intelligence, and cloud computing. Contemporary digital data also exist in a variety of forms, such as images, text, and graphs. This requires the application and development of specialised algorithms, for example from image analysis, natural language processing, and social network analysis. In addition, data often varies in time. Therefore, data streams and algorithms for longitudinal and temporal data analysis are often important elements in Data Science applications.
In Data Science, technical topics such as data models and algorithms are often influenced by ethical and legal considerations. An example is our research on decentralised and privacy-preserving data analysis.
Research topics
- Data Analytics, Integration, and Visualization: data curation, collection, analytics and integration for intelligent and autonomous systems.
- Databases: big data, keyword search and ranking, (semi) structured data, (attributed) graphs, semantic data, spatial data, online (geo) social networks.
- Distributed Computing Infrastructures: performance, efficiency, and optimisation of large-scale computational resources.
- Federated Machine Learning: federated learning, storage management, data compression, security and privacy, simulation and sampling.
- Learning, Inference and Optimization: probabilistic ML, deep learning, robust learning, Bayesian and simulation-based inference, inverse problems, statistical sampling, active learning, global optimization.
- Network Science: multilayer networks, temporal text networks, probabilistic networks.
- Social Data Science: social network analysis, analysis of online communication, analysis of register data, online information disorder.
Research entities
Research groups at the department:
- CPS-Lab – Cyber-physical Systems Lab at Uppsala University
- InfoLab – Uppsala University Information Laboratory
- SciML – Scientific Machine Learning group
- UDBL – Uppsala Database Laboratory
- UppsalaVLL – Uppsala Vision, Language and Learning group
We are also part of eSSENCE, a strategic collaborative research programme in e-science between Uppsala University, Lund University, and Umeå University.
Faculty members
- Georgios Fakas (UDBL)
- Didem Gurdur Broo (ICPS-Lab)
- Andreas Hellander (SciML)
- Matteo Magnani (InfoLab)
- Christian Rohner (InfoLab)
- Prashant Singh (SciML)
- Salman Toor (SciML)
- Ekta Vats (UppsalaVLL)
- Davide Vega D'Aurelio (InfoLab)
Research awards
- The Nordic observatory for digital media and information disorder (NORDIS) won the Chydenius medal.
Education
Courses in data science are part of the following programmes:
- Master's programme in Data Science, spec. in Data Engineering.
- Master's programme in Data Science, spec. in Machine Learning and Statistics.
- Master's programme in Image Analysis and Machine Learning.