Authors:
Kurt Englmeier
1
and
Fionn Murtagh
2
Affiliations:
1
Schmalkalden University of Applied Science, Germany
;
2
University of Derby, United Kingdom
Keyword(s):
Data Science, Big Data, Information Discovery, Data Analysis, Data Mining, Text Mining, Information Extraction, Information Usability, Semantic Layer.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Big Data
;
Business Analytics
;
Data Analytics
;
Data Engineering
;
Data Management and Quality
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Semi-Structured and Unstructured Data
;
Symbolic Systems
;
Text Analytics
Abstract:
Data Scientists are the masters of Big Data. Analyzing masses of versatile data leads to insights that, in turn, may connect to successful business strategies, crime prevention, or better health care just to name a few. Big Data is primarily approached as mathematical and technical challenge. This may lead to technology design that enables useful insights from Big Data. However, this technology-driven approach does not meet completely and consistently enough the variety of information consumer requirements. To catch up with the versatility of user needs, the technology aspect should probably be secondary. If we adopt a user-driven approach, we are more in the position to cope with the individual expectations and exigencies of information consumers. This article takes information discovery as the overarching paradigm in data science and explains how this perspective change may impact the view on the profession of the data scientist and, resulting from that, the curriculum for the educ
ation in data science. It reflects the result from discussions with companies participating in our student project cooperation program. These results are groundwork for the development of a curriculum framework for Applied Data Science.
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