The Impact of Data Science on Social Sciences | Laissez-Passer
The Impact of Data Science on Social Sciences
Data science methods are of increasing importance for the social sciences. Data Scientists from different sectors provide new approaches to testing social science theories and substantially expand the canon of methods of empirical social research.
Data Science in Social Sciences
The analysis of incomplete, erroneous, and time-resolved data on social systems makes demands that are at best partially met by existing methods, e.g. social network analysis or machine learning. This results in new challenges for computer science, both for research and for the training of a generation of critical data scientists.
While the critical reflection of empirical research methods in the social sciences is an important aspect of education, computer science courses often lack both statistical and scientific-theoretical foundations, which are of outstanding importance for the use of data science methods in the sciences.
Despite all the Sunday speeches about the importance of interdisciplinary research, there are considerable cultural and scientific-political hurdles that stand in the way of effective cooperation between computer scientists and social scientists. The dismantling of these hurdles in research funding, training, and scientific incentive systems and structures is of crucial importance for the further development of the social sciences and computer science.
The Impact of Big Data on Our Society
Collecting and analyzing data is already having a major impact on society. This influence will grow in the future. Among other things, more and more data is being collected and it can be checked whether the analysis and the conclusions drawn from them were successful. In addition, technological development is always progressing. Here’s a glimpse into the possibilities that come with big data:
Big data can be used to forecast the development of diseases such as Alzheimer’s, diabetes, and cancer. By collecting data about the way people who develop these diseases live, patterns in these diseases can be identified. Among other things, habits, sports behavior, and nutrition are analyzed here. This allows risk behaviors and groups to be determined.
By raising awareness of risk behavior and by providing early support to risk groups, these diseases can be treated preventively. However, these predictions must also be treated with caution. Such analysis can lead, for example, to people who belong to a certain risk group being disadvantaged. For example, they have to pay a higher health insurance premium. Or because they do not hire because the employer fears that this person will fall ill and thus create an economic disadvantage for him.
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Big data also makes it possible to understand connections as a whole. It is interesting, for example, how the social background of a person is related to health, level of education, criminality, but also the possibility of social advancement and decline. Findings of these connections could change social structures. For example, this can result in better equal opportunities. However, it is also possible that the gap between rich and poor will continue to widen.