Data Science

Our expertise in Data Science is reflected by and related to several lectures, projects, as well experts.

Related Research Areas

Information Visualization and Visual Analytics are both closely related to Data Science. GRIS combines a broad set of competences reflected by supported data types, analysis techniques, and application areas.

Machine Learning , Interactive Machine Learning and Data Mining emphasize algorithms used to conduct Data Science tasks.

Data-centered Research at GRIS

Interactive Data Generation follows the principle to create data sets in a visual-interactive way.

Multivariate and Mixed Data Analysis focuses on large and complex data sets with many dimensions/attributes/features, i.e., tabular data ready for in-depth analysis and Knowledge Generation.

Spatio-Temporal Visualization is the research in visualization and interaction techniques for data with geographical and temporal information.

Time Series Analysis supports users in the analysis of time series (time-oriented) data. Visual-interactive interfaces play a key role, as well as meaningful Data Mining and Machine Learning techniques for time series data.

Uncertainty Visualization focuses on the communication of uncertainty information about data and models in a visual way. GRIS conducts research for a variety of data types and related uncertainties.

Data Science, or data-driven science, seeks to provide information (and extract knowledge) from large unstructured data. It includes and combines a series of theories and techniques from math, statistics, information technology, machine learning, data mining, to data visualization.

The increasing of complex and undiscovered data is among the most motivating factors for data science.

While the origins of data science have been based on statistics decades ago, Data Science is now established in all data-centered research and application areas all over the world, for over ten years. Data Science is referred to a solution for challenges with big data and business intelligence, which is required in virtually any application field and sector.

Technical fields within Data Science are multidisciplinary evaluations, data models, data analysis algorithms, data-centered tools and frameworks, as well as theory.

Areas of expertise associated to Data Science are computer science, data bases, programming, librarians and archivists, as well as software engineering.

A data scientist may be involved in the collection, processing, storage, retrieval, analysis, and interpretation of data to help a collaborating subject with the latter data-related tasks. While in practice data scientists do not necessarily need to be scientists in the lab, we particularly emphasize the scientific aspects of Data Science.