In a world where there is a need to pursue programmes, which equip students with skills that employers demand, the sharing of data resources, collaborative activities, and a culture of drawing from each other’s work has become all too important. “An education system that does not equip social science students with tech-driven applications is greatly limiting their career prospects,” says James Abdey, associate academic director of the University of London programmes at the London School of Economics (LSE). He explains, “Take the case of a Geography graduate for instance; there is a tremendous demand for Geographic Information Systems (GIS) analysts who are required to routinely deal with large datasets to process and analyse into user-focused displays such as graphics, maps and charts. This work requires extensive knowledge and experience with GIS techniques, technology, and principles, as well as strong proficiency with computers, including R, Python, HTML and Microsoft Office software.”
“Today, an Economics graduate will be highly valued as a compliance analyst, with responsibility for ensuring that compliance has been achieved internally as well as externally for a company. Similarly, a graduate who is aiming for public policy can conduct quantitative research using advanced computational and data science techniques such as machine learning (ML),” says Abdey.
While a social science degree can get one started on a wide array of careers in economics, management, social work, law, academia and policymaking, the key here is to have had the relevant exposure 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,” adds Abdey.
Data science in decision-making
One of the key challenges for decision makers and managers is to understand what makes for good data science, and how the evidence from this field should be used in evaluation and decision-making. “Effective use of data and machine learning tools is critical in making adaptive and personalised policies that improve the standard of living and paves way for the development of society. If integrated well into the policy making process and understood well by policymakers, data analysis has tremendous potential to lead to better decisions. Policy-makers in state departments, for instance, can avail data science to tackle social issues such as traffic, road safety and crime,” says Abdey.
“Specific data science tools like R and Python, along with Tableau, will do wonders for various tracks of Social Science. Then there is Deep Learning, a part of the ML family. At a ‘deeper’ level, it helps to understand hierarchies, and patterns that can help a system to learn complex functions mapping the input to the output directly from data, without depending entirely on human-made features,” says Abdey.
He feels social scientists must shed their inertia and train themselves in data science techniques because these are, ultimately, languages and can be acquired with a finite amount of effort.
More importantly, the data science techniques can help take the investigation of social science questions much deeper; hence facilitate better and smoother social decision-making.