In a world in which it is possible to amass large quantities of data, it becomes necessary to have tools that separate the signal from the noise. In Data Science, students are introduced to a variety of algorithms and statistical models to evaluate datasets using the programming language R.
Evaluating a dataset involves formulating the right research question and assessing the qualities of the dataset that lend themselves to different statistical models, including non-linear regression, Bayesian analysis, and random forests. Students learn these skills at a fundamental level in addition to trying them out in the real world with a capstone project of their own design.
Through this project, students have pursued topics they are passionate about such as stock market predictions, emotional reactions to artwork, cancer diagnosis, and more.