Training Future Leaders to Master Policy and IT
As demand for data analysts in government grows, what may be the nation's first master’s program that teaches not just public policy knowledge but technology skills too has launched.
Data-driven decision-making is becoming an increasingly important part of government management. But public policy schools still tend to treat data analysis as a very separate, segregated function. That could be changing. This year, the University of Chicago launched a two-year master’s program that blends public policy education with IT skills. Envisioned as an academy to train future government CIOs and data scientists, the hybrid program may be the first of its kind.
Students at the Harris School of Public Policy still take traditional courses in economics, political science and statistics, but they’re also exposed to computer programming, data analytics and machine learning. The degree is the brainchild of Harris School senior fellow Brett Goldstein, who spent two years overseeing data and technology for Chicago Mayor Rahm Emanuel. Like many computer programmers who take jobs in government, Goldstein started in the private sector, at the online restaurant reservation portal OpenTable.
During his time in government, Goldstein says he saw a need to build a pipeline of public employees who understand information technology, especially in the context of contracting with private firms to build apps and manage public databases. “Often you’ll have chief technology officers in government who come from a nontechnical background, such as the budget office,” he says. But the fact is that “you need the ability to understand what you’re procuring and why it is breaking down.”
While the Harris School may be the first to offer a joint master’s degree in computational analysis and public policy, it borrows from curriculum developed a few years earlier at Carnegie Mellon University. Daniel Neill, who holds a Ph.D. in computer science, teaches an introductory course at Carnegie Mellon on the policy applications of machine learning, data mining and artificial intelligence. Neill has personal experience using large data sets to inform policy. He’s currently helping the city of Chicago understand and manage rodent infestations. “We can use data from 311 nonemergency calls for rodent complaints,” he says. Using the call data, the city can try to predict where the next cluster of complaints will emerge and suggest interventions ahead of time. Similar approaches could help reduce violent crime and the spread of communicable diseases.
Both graduate programs are responses to a growing demand for data management and analysis skills in the public sector. The Harris school students won’t be able to build Web applications, but they’ll understand enough about how coding works to manage IT projects and make better-informed decisions. Certainly, some high-profile crises (such as a failing health-care exchange site) will demand more specialized technical skills. But like rodent infestations, Neill says, “not every problem gets that sort of public attention.”
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