Due to recent advances in computational techniques, inferences have become possible with the precision of detail that never existed before. Then there is a maturity that has evolved in the research community which has encouraged greater transparency, sharing of data resources, collaborative work and a culture of drawing from each other’s work.
It is conceivable today to propose thousands of what-if scenarios to better understand the consequences of certain policies, which was never the case earlier. With the help of data science tools it is possible to develop alert systems (by spotting anomalies), discover patterns in data (and even in text, using tools like text mining) to identify relationships, as well as segment a population into useful clusters. With the vast amount of information and research now available in Social Sciences, it is possible today to extract the kind of insights unthinkable even a few years ago.
The analysis of such data can lead to marked improvements in policymaking, whether at the level of an organization, a commune or cooperative society, a state or a country. This is why the field of data science promises so much in terms of being able to improve people’s lives, and has wide applications in the social sciences, especially when interpreted somewhat broadly to include management and finance.
A higher education programme in Social Science, particularly a graduate programme, should necessarily include tech-driven subjects in the curriculum to ensure that students have a diverse range of careers to choose from. For instance, an Economics graduate today will be highly valued as a Compliance Analyst, with responsibility for ensuring that compliance has been achieved internally as well as externally for an organization. Similarly, a graduate who wants to contribute to public policy can conduct quantitative research using advanced computational and data science techniques such as machine learning.
Thus, career prospects include economics, management, finance and urban planning, but also social work, law, academia and policymaking. The key here is to have had the relevant expose to tech applications and data analysis tools needed for the job. Superior career prospects, job retention, promotion prospects, as well as a marked higher pay bracket are a clear advantage for people with the right technical skills.
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Given the urgency of training social science students in the intricacies of modern day technology, most prominent higher education institutions have inculcated Data Science course in their curriculum. For instance, the London School of Economics (LSE) recently launched its MSc Data Science degree which provides training in data science methods, with a focus on statistical and machine learning perspectives. Students learn about computing principles and gain hands-on experience in using state-of-the-art big data analytics systems, enabling them to apply advanced methods of data science and statistics to investigate real world questions. Core courses include: Computer Programming, Managing and Visualising Data, Data Analysis and Statistical Methods, and Machine Learning and Data Mining.
In keeping with its spirit of enabling graduates to apply their knowledge to solve social problems all over the world, LSE has also created Data Science courses under the University of London programmes umbrella. These include a BSc Data Science and Business Analytics programme, and a Graduate Diploma in Data Science, which is being offered in India by the Indian School of Business & Finance (ISBF). So now students in India, and indeed in other parts of the world, can benefit from LSE’s Data Science programmes, without necessarily having to come to the UK to pursue them.
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