Theoretical Data Science: bridging the gap between domain-general and domain-specific studies

The entering into big data era gives rise to a novel discipline called Data Science.Data Science is interdisciplinary in its nature,and the existing relevant studies can be categorized into domain-independent studies and domain-dependent studies.The domain-dependent studies and domain-independent ones are evolving into Domain-general Data Science and Domain-specific Data Science.Domain-general Data Science emphasizes Data Science in a general sense,involving concepts,theories,methods,technologies,and tools.Domain-specific Data Science is a variant of Domain-general Data Science and varies from one domain to another.The most popular Domain-specific Data Science includes Data journalism,Industrial Data Science,Business Data Science,Health Data Science,Biological Data Science,Social Data Science,and Agile Data Science.The difference between Domain-general Data Science and Domain-specific Data Science roots in their thinking paradigms:DGDS conforms to data-centered thinking,while DSDS is in line with knowledge-centered thinking.As a result,DGDS focuses on the theoretical studies,while DSDS is centered on applied ones.However,DSDS and DGDS possess complementary advantages.Theoretical Data Science(TDS)is a new branch of Data Science that employs mathematical models and abstractions of data objects and systems to rationalize,explain and predict big data phenomena.TDS will bridge the gap between DGDS and DSDS.TDS contrasts with DSDS,which uses casual analysis,as well as DGDS,which employs data-centered thinking to deal with big data problems in that it balances the usability and the interpretability of Data Science practices.The main concerns of TDS are concentrated on integrating the data-centered thinking with the knowledge-centered thinking as well as transforming a correlation analysis into the casual analysis.Hence,TDS can bridge the gaps between DGDS and DSDS,and balance the usability and the interpretability of big data solutions.The studies of TDS should be focused on the following research purpose:to develop theoretical studies of TDS,to take advantages of active property of big data,to embrace design of experiments,to enhance causality analysis,and to develop data products.