Data Science is the study of the generalizable extraction of knowledge from data, the key word being science in this definition.
It incorporates varying elements and builds on techniques and theories from many fields, including signal processing, mathematics, probability models, machine learning, statistical learning, computer programming, data engineering, pattern recognition and learning, visualization, uncertainty modeling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products.
Data Science is not restricted to only big data, although the fact that data is scaling up makes big data an important aspect thereof.
At the same time this new field of scientific study has to address different sensitivities, including:
The above are complemented by different kinds of data analytics, including predictive analytics: market penetration and sustainable company strategies depend on the right decisions at the right time. With increasing amounts of market and competitor behaviour data to analyze for decision makers, predictions become vulnerable without the right kind of know-how.
Major application domains of the above research components include, but are not limited to, biology, drug discovery, digital preservation, cultural studies, automation, business logistics, etc.
More about the project in Data Science.
Research leader: Sándor Darányi, professor in Information Science.