A data science axiology: the nature, value, and risks of data science

Data Systems Laboratory, School of Engineering and Applied Sciences Harvard University, Cambridge, MA USA =============DRAFT July 18, 2023====================== Data science is not a science. It is a research a theory of value that defines the nature, value, paradigm. As data science is in its surpass science - our most powerful research infancy, its axiology can only be speculated. Such paradigm - in enabling knowledge discovery that an axiology can aid in understanding and defining is changing our world[10]. This paper explores and data science and recognizing potenUal benefits, evaluates its remarkable, definiUve features. We present the history and nature of data science and offer Modern data science is in its infancy. Emerging candidate definiUons of essenUal data science slowly since 1962 and rapidly since 2000, data concepts required to discuss its axiology. Within a science is a fundamentally new field of inquiry, decade, this remarkable new research paradigm one of the most acUve, powerful, and rapidly will be seen as a milestone in human knowledge evolving innovaUons of the 21st century. Yet we are just beginning to data science as a Promethean Moment[10] that understand and define it. Due to based on single invenUons, e.g., the prinUng press, its infancy, many definiUons are independent, this moment is based on a meta-technology Essen'al data science concepts data science community to achieve such a Data science (the data science research paradigm) definiUon. To problem solving based on its unique ability to contribute to an iniUal assessment and definiUon computaUonally analyze data to discover insights of data science, this paper proposes an iniUal into moUvaUng domain problems where the axiology of data science. A comprehensive data science axiology is (i.e., learning from data) of data science research A meta technology is used to produce new technology and knowledge hence can be applicable to most human endeavors. Data about, discover, arUculate, and validate the true science results are probabilis5c, correla5onal, nature of the ul5mate ques5ons about natural, possibly fragile or specific to the analysis method observable phenomena as new knowledge about or dataset, cannot be proven complete or correct, those phenomena. ScienUfic results are defini5ve, and lack explana5ons and interpreta5ons for the conclusive, casual, robust, universal knowledge of mo5va5ng domain problem[46]. Like all research paradigms, science and discovery conducted by applying the data science data science are complementary.