What is Data Science? Thinking about its use in the enterprise
The data science domain is one of the academic fields that has been reaffirmed its value in recent years with the rise of AI and WEB3 (web3.0). Data science is a job title within a company that utilizes BI tools, but is itself effective in the process of mathematical and logical approaches that govern AI, and is utilized in the pre-verification stage and requirement definition for efficient machine learning. One of the tasks is to read the objective and explanatory variables, plot correlations and trends, and so on.
In addition, the data science domain will be fully utilized to define and organize the requirements for how to identify and what to use the large amount of interactive, automated data that will come and go in the blockchain in the Web3 stage.
Wikipedia describes data science as follows.
It refers to an approach that attempts to draw new scientific and socially beneficial insights from data, and in this context, it covers a cross-section of data-handling methods: information science, statistics, algorithms, and so on. Data science can also be overviewed from statistical, computational, and human perspectives. Each perspective is an essential aspect of data science, and the organic combination of these three perspectives is the essence of the discipline of data science (Blei and Smyth, 2017[3]). The lack of awareness of the importance of field knowledge in data analysis to date is thought to be the source of the widespread misunderstanding of the discipline of data science (Hernan, Hsu and Healy, 2018) Data science has a distinctly applied context and a hyperdomain aspect, and it requires clear social accountability for research results, as well as additional standards for quality control beyond the traditional classroom standards for quality assurance of research results. Organizational heterogeneity is also important for the effective promotion of data science. Science that meets these requirements can be recognized as a type of Mode 2 science, as argued by Gibbons et al."
The data science domain is attracting attention across occupations and industries, not only from large corporations, but also from small and medium-sized businesses and even one individual store. Individuals and organizations want to science historical data and apply them to their business models and customer experiences.
With the current situation being taken up in many academic fields in recent years, there is great potential in Japan, as in Europe and the United States, to become a market where "data scientists," who analyze data and conduct activities to implement them in management, can be pursued as a single professional field.
