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Data and decision science

Data science is the inter-disciplinary field which aims at the discovery of patterns and hidden information in (big) datasets, following an analytics process. Towards that end, of knowledge discovery, data science apply a blend of techniques extracted from different disciplines e.g., machine learning and statistics, on various types of big data(sets). The ultimate purpose of analyzing the data is to support the decision-making process. Analytics output could also serve other purposes such as enabling the digital transformation of organizations. Recently, analytics has been augmented in several industries and contributed towards solving societal problems.
 
Subgroups:
•    Data Analytics: This subgroup investigates the data, big & small, that originate from within the organization and from outside. Towards that end, we investigate how the data should be managed, pre-processes and later analyzed. We use multiple analysis methods which include statistical methods, machine learning and deep learning models.

•    Decision-making: This subgroup explores one of the most important skills for any organization/society, that is, decision-making. We investigate the theory of how to make goal-oriented decisions which involves the following elements: the problem; the decision maker; the decision; the data; and analytics. We take a contemporary approach towards decision-making where we study evidence-driven decision making and the factors which influence organizational successful adoption. We further investigate how to make decisions more ethical and transparent/explainable.

•    Digital technologies & the organization: In this subgroup, we investigate the interplay between technologies, humans and the organizations. Such interplay unfolds in different occasions e.g., implementing new technologies and systems such as 5G, enterprise systems, IoT, etc. Additionally, we also investigate the enabler and barriers of successful adoption of technologies in organizations and the society at large.