Why We Should Teach Public Policy Students More about Technology (and Coding)

Over the last several years, a fascinating trend has emerged among students applying and entering to Harvard’s Kennedy School of Government. A small but fast-growing number are taking classes in data science and machine learning. They’re asking professors for more direct coding experience – and increasingly are critical of the skills imparted by required courses in statistics and data. They’re filling up optional tutorials offered by more tech-savvy parts of the University on GIS, data visualization, and web scraping. There is a broad, large interest in learning more about technology – especially the interaction between technology and governance. This change is being driven by student demand, not supply – and there’s a risk that supply is moving too slowly.

In thinking about their future roles in government, policy students have recognized the need to learn three key skills that policy schools have been slow to offer. First, open data and the internet have created huge sources of messy data; students see that unlocking this data could have a big impact on the public good. Second, students who plan to work in positions where they build, buy, and manage technologies know that they need competence and confidence in understanding the relationship between technology and policy. Third, running and implementing advanced analytics in policy demands some fluency with more (and more powerful) programs than the traditional policy toolkit.

The internet and open data have created a massive opportunity for students with technology skills to solve public problems. Thirty years ago, policy programs viewed teaching students the granularities of data extraction and management as a waste of time. Our goal was helping students turn data into insights into recommendations into policies – and datasets at the time were largely limited in number and scope, expensive to produce, relatively high quality, and created for statistical analysis by agencies and economists. Today, nearly the opposite is true, and policymakers are awash in rich, messy datasets coming from myriad public and private sources. Learning how to write a script that can pull down, clean, and restructure data from weather stations, or run a language-processing algorithm on complaints to a financial regulator isn’t just an academic exercise. It’s a core skill set which can give policy students the edge, and give future managers the capacity to achieve their teams’ full potential.