Computing in Context, a computer science course for liberal arts majors, expands with new SIPA track

Computing in Context has added a new track for the School of International and Public Affairs (SIPA) to teach computational concepts and coding in the context of solving policy problems. SIPA students will be taught by both a computer science professor, who lectures on basic computer and programming skills while teaching students to think like computer scientists, and by a SIPA professor who shows how those skills can augment traditional policy analysis. Projects and assignments will be geared for the policy arena to give students a command of technical solutions for problems they are likely to encounter in their classes and future work.

SIPA’s is the first new track to be added since Computing in Context debuted in spring 2015 with tracks in digital humanities, social science, and economics and finance. Aimed at liberal arts majors who might not otherwise take computer science, Computing in Context is the first of its kind to provide a contextualized introduction that combines algorithmic thinking and programming with projects and assignments from different liberal arts disciplines.

Gregory Falco

How much should students in the School of International and Public Affairs (SIPA) know about computer science?

In a digital world when information is being collected at unprecedented rates and as government decision-making becomes more data driven, computer science is fast becoming fundamental to policy analysis. Computational methods offer an efficient way to navigate and assess a variety of systems and their data, and make it possible to comb even massive data sets for subtle patterns that might otherwise go undiscovered. A relatively small amount of code can replace tedious, time-consuming manual efforts to gather data and refine it for analysis.

As machine learning and text mining turn texts into data analyzable by a computer, computational methods once reserved for quantitative data can now be applied to almost any type of document—emails, tweets, public records, transcripts of hearings—or to a corpus of tens or hundreds of thousands of documents. These new methods for computationally analyzing texts and documents make computer science relevant to humanities and social science disciplines that traditionally have not been studied computationally. Social science majors may analyze vast numbers of social media posts, English majors may automate stylistic analyses of literary works, finance students may mine data for new economic trends.

Liberal arts students have been increasingly skipping the cursory computer science class intended for non-majors (1001) and enrolling in computer science classes alongside computer science majors. Adam Cannon who has been teaching introductory computer science for 15 years has watched the number of liberal arts students in his classes climb. From being outnumbered in his classes 4-to-1 five or six years ago, liberal arts students last year surpassed the number of computer science majors in his classes. “These students want more than an appreciation of computer science; they want to apply computer science techniques in their own fields.”

To think like a computer scientist

Though his classes had the rigor students were demanding, Cannon worried that classes and projects meant for computer science and engineering students were too focused on numeric processing to be immediately relevant to liberal arts students studying mainly texts, and who might walk from his class into a lecture on Jane Austen or the evolution of liberation theology in Latin America

To provide context for liberal arts students and demonstrate how computational methods relate to their fields, Cannon introduced last year a new computer science class, Computing in Context, to focus on text processing. A hybrid class taught by a team of professors, it combines basic computer science concepts and Python programming with lectures and projects by humanities and social science professors who show how those skills and methods apply to a specific liberal arts discipline.

The computer science component is rigorous. While covering basic computer and programming concepts, the class teaches students to think algorithmically so they can, like computer scientists, reformulate problems in a way they can be analyzed by a computer. A framework for problem solving, algorithmic thinking forces students to think hard about a problem and see it clearly enough to break it down into its component parts. It’s a logical, step-by-step approach that entails critical thinking and deductive reasoning and has wide application across different types of problems. Perhaps more than any other single subject, computer science bridges the logical reasoning and abstract and creative thought that should be an essential part of college education.

Computer science within a context

Algorithmic thinking is critical for designing solutions to new problems and analyzing new data sets, but the nature of the problems and the data sets depends on the particular field of study. Different liberal arts disciplines require different kinds of computational proficiency; for this reason, Computing in Context maintains separate tracks for each discipline, with each track taught by a different professor. The class debuted with three tracks: social science (Matthew Jones), digital humanities (Dennis Tenen) and economics and financing (Karl Sigman). All students take the computer science component and learn the same basic concepts, but then divide into separate tracks to learn how those concepts apply to their particular discipline.

It’s a modular design that makes it easy to insert additional tracks as more departments and professional schools act to make computer science part of their students’ curriculum. The first time a new track is offered, a professor from that department lectures live, and then records those lectures for future semesters. This flipped classroom approach—where students view videos of lectures outside class and use classroom time to discuss the content of those videos—helps make the class financially sustainable since each new track represents a one-time expense.

SIPA’s is the first track to be added since Computing in Context was introduced and is being taught by Gregory Falco, a Columbia adjunct faculty who is also an executive at Accenture and is currently pursuing his PhD in Cybersecurity of Critical Urban Infrastructure at MIT. With an MS in Sustainability Management from Columbia University, Falco specializes in applying data, analytics, and sensors to solve complex sustainability and security policy problems.

Having Falco teach a track within Computing in Context is part of SIPA’s commitment to deeply integrating technology courses into its curriculum and equipping students with a robust tech and computer science skillset. It is one way SIPA Deans Merit Janow and Dan McIntyre along with Falco are pioneering the next generation of policy education.

What SIPA students can expect

For the first six weeks of the course, SIPA students will attend the twice-weekly lectures on computer science along with all other students. At the halfway point, the track lectures kick in, and SIPA students go to lectures given by Falco, who will also assign homework and projects geared specifically to public policy. While economics and financing students price options and digital humanities students run sentiment analysis on tweets, SIPA students might be troubleshooting sources of environmental pollution, evaluating the effectiveness of public housing policy, or determining the impact of local financial markets on international healthcare or education.

Considering SIPA is a professional school, Falco’s lectures and assignments are aimed at helping students integrate and transition what they learn in the classroom to the professional setting and job market.

Unlike other tracks, the SIPA track will always have live lectures each time it is given. The changing relevance of policy problems requires a class constantly evolving for current events. Also, the skills SIPA students learn in Computing in Context will be integrated into their capstone research projects that serve as graduate theses; since Falco teaches both Computing in Context and will advise research projects, his constant, in-class presence will provide a more continuous resource of expertise on data and computing for SIPA students.

“This is a one-of-a-kind, very cool policy class because it enables SIPA students to think like computer scientists and see the art of the possible in relation to how technology, data analytics, and artificial intelligence can be used to address policy problems,” says Falco. “Beyond coding, the class helps foster the language of digital literacy which is invaluable in the professional world for policy practitioners.”

The SIPA track will be the first test of how well Computing in Context can scale to meet demand, which is only expected to grow as more departments and schools like SIPA integrate computer science into their curricula.

Posted: 9/20/2016

 – Linda Crane