I've noted in several blogs over recent months the proximity of the business-led discipline data science with academically-focused quantitative social science. From my perspective, DS obsesses on data, statistical and computational methodologies to study social behavior, much the same as QSS.
Addressing the pressing need to recruit suitable university graduates for careers in data science, the Nokia mother-daughter data science team of Amy O’Connor and Danielle Dean made the case at Strata-Hadoop World 2012 for college grads with math/stats, bioengineering and social science educational backgrounds.
The two authors of O’Reilly's excellent “Machine Learning for Hackers” are both practicing data scientists pursuing Ph.D.'s – one in psychology and the other in political science.
Rachel Schutt, originator of the Introduction to Data Science course at Columbia University, has a Ph.D. in political science. Schutt's course objective and take on data science? “This course is an introduction to the interdisciplinary and emerging field of data science, which lies at the intersection of statistics, computer science, data visualization and the social sciences.”
The new masters in Computational Science and Engineering at Harvard is being touted as a cross-disciplinary program “with the aim of training new leaders for a future where large-scale computation and advanced mathematical modeling will propel discovery and innovation in fields from psychology to photonics…. The Harvard program will offer a curriculum broader than typical for master’s degrees in computational science, anchored by core courses in both computer science and applied mathematics and embracing a wide range of applications, including the social sciences in particular.”
Finally, the pioneering research work of sociologist and now-Microsoft researcher Duncan Watts is the archetype for the amalgam of data and social science, bringing methodological and computational tools to bear on knotty business social science questions. Watts’ keys are “agility, field experiments, open innovation, crowdsourcing, data and analytics.” The author lauds the science of business manifesto outlined by MIT professors Erik Brynjolfsson and Michael Schrage for its potential of bringing hypotheses, experiments and analytics to all facets of business.
Watts is at it again with his informative Harvard Business Review blog, “The Importance of Studying the Obvious.” The problem with “obvious” and “common sense” explanations of social behavior is that they’re often as not incorrect – quick, gut, intuitive, simplistic – and wrong. Think of Watts’ common sense as Nobel prize-winning psychologist Daniel Kahneman’s “Thinking Fast” which serves us well for simple decisions but can lead astray for complex ones. Watts’ scientific “uncommon sense” in contrast, looks a lot like Kahneman’s more deliberate and evidence-based “thinking slow,” which should be the foundation of complex business decision-making.
The Watts’ antidote for the limitations of intuition/common sense? “One interesting possibility is raised by the arrival of "big data," increasingly derived from digital communications, social media, mobile apps, and e-commerce sites ... For this reason, companies like Facebook, Google and Microsoft, where I now work, are beefing up their research labs both with computer scientists, who have the technical skills to handle huge datasets, and social scientists, whose job it is to ask the right questions. In fact, the emerging intersection of computer and social science – what some people are calling computational social science – is one of the hottest areas of research today.”
While CSS is in its infancy, there's a growing cadre of quantitative social science luminaries busy promoting its inevitable growth. “The capacity to collect and analyze massive amounts of data has unambiguously transformed such fields as biology and physics. The emergence of such a data-driven ‘computational social science’ has been much slower, largely spearheaded by a few intrepid computer scientists, physicists, and social scientists. If one were to look at the leading disciplinary journals in economics, sociology, and political science, there would be minimal evidence of an emerging computational social science engaged in quantitative modeling of these new kinds of digital traces. However, computational social science is occurring, and on a large scale, in places like Google, Yahoo and the National Security Agency.”
In contrast to the limited, traditional social science data acquisition methods like the self-reporting survey, “[n]ew technologies, such as video surveillance, e-mail and ‘smart’ name badges offer a remarkable, second-by-second picture of interactions over extended periods of time, providing information about both the structure and content of relationships.” Interestingly, the academics are unsure which discipline will ultimately own CSS. “In the longer run, the question will be: should academia be building computational social scientists, or teams of computationally literate social scientists and socially literate computer scientists?”
I like the computational social science coinage a lot. It connotes science, big data, statistics and computation. Look for the computational social scientists of the academic world to push the frontiers of data science to the benefit of business – and for data scientists to simultaneously elevate CSS to the benefit of academia.
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