Many industries are revolutionised through advanced technologies and have made use of big data to be increasingly efficient. Despite the significant progression in primary research, the success rate of present big data applications in social sciences are limited.
In basic sciences, the emphasis is on the technical prerequisites to better record and store large amount of data and automatically processing them; and artificial intelligence designs such as machine learning seems to play a relative significant role. However, social sciences have failed to truly benefit from it. Rather, many social scientists are constantly confounded by the available opportunities.
The data-driven approach to social phenomena also known as computational social sciences can help to explain research questions in social sciences; however, it cannot develop similar questions on its own. The findings of statistical correlations cannot supersede the scientific clarifications of causal effects. Social sciences often question both the “what” and “why”; thereby making social scientists indispensable. This is because innovative frameworks of social interactions that are intentionally designed to also calibrate and validate against large, previously unavailable quantities of data. Hence, new methodological expertise is essential and universities will have to prepare students for it.
However, on the contrary, the engineering sciences can benefit from the social sciences. Technical systems today are dependent on social dimensions. It is not feasible to conceptualise a smart energy supply or shared platform for software development without taking into consideration the human behaviour and social relations – and this is precisely the core skills of social sciences. Therefore, educators should begin looking at interdisciplinary teaching approaches.
Source: Phys Org
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