Project: Muaad Kholi - Introduction to Computational and Data Sciences (Spring 2017)

Rapid advances in computing power have elevated computational science as a third mode of scientific practice that is distinct from the traditional modes of experiment and theory. These advances have also contributed to the boom in the interdisciplinary field of data science, where vast amounts of data are analyzed to make predictions and determine trends. As computational and data science becomes more commonplace, it will continue to drive technological change, inform government policy, be utilized in commercial enterprise, and further intertwine with many of the career paths that new college graduates will enter. To prepare students for this future, this first half of the course will guide students in developing basic skills and intuition in data processing, analysis, and visualization using the statistical programming language R. This training culminates in a mid-term project where students use these skills to summarize and present a real-world dataset of their choice. In the second half of the course, topics in computational modeling and simulation such as regression, decision trees, model evaluation, and stochastic models (such as Monte Carlo methods) will be covered. The course as a whole emphasizes the importance of reproducible research as a scientific principle and a documentation practice, along with basic guidelines for interpreting and evaluating data trends and model outputs. During the course, connections with the covered topics and current events and technology will be brought up for discussion and reflection. The power and limitations of the scientific method in the context of data-driven modeling and simulations will be emphasized, which demonstrate how scientific literacy and a competency in statistics are indispensable tools to making sense of our modern world.