Computational Modeling in the Natural and Social Sciences
This course is designed for future scientists, engineers, researchers, business entrepreneurs, and financial analysts and introduces them to using computational models to understand natural and social phenomena. It draws upon the latest tools and research.
(This course is targeted at children entering Grades 8, 9, 10 in 2020-21)
(The course does not require any prior knowledge of programming. It introduces students to block-based and text-based programming.)
Learning or investigating how things work in the natural and social sciences can be difficult given the complexity of the phenomena involved. We cannot look into people's minds or see how exactly a forest fire spreads; nor can we see molecules or electrons or their movements. Another difficulty is that cause and effect are not simple and linear; for example- building roads to reduce traffic congestion can result in more cars coming onto the road increasing congestion. This makes it hard to understand many things in various disciplines from physics to chemistry, from biology to ecology, from economics to political science, from marketing to investment banking.
The advent of fast computational tools has enabled us to build and analyze more complex models to understand such complex problems. Thus computational models are very important tools of investigation in the disciplines mentioned above. This course is designed for future scientists, engineers, researchers, business entrepreneurs, and financial analysts so that they get introduced to this new way of computationally thinking and understanding natural and social phenomena in the world. The course draws upon the latest tools and research from Northwestern University's Learning Sciences Department, and other organizations in this field. Specifically the course will use NetLogo and NetTango- computational modeling environments, built keeping both middle-school children and researchers in mind.
Students will study various phenomena including how a forest fire spreads, and the motion of an object under free fall through computational models. Through this, they will engage in scientific inquiry and also understand non-intuitive effects, such as a tipping point. They will also learn how such models are created by looking at the underlying code, and how to perform a systematic research investigation by generating, collecting, visualizing and analyzing data. The course does not require students to write code, while providing students interested in coding, to do so if they wish.
Learn more about our expert course facilitator, Sugat Dabholkar, here.