DSC - Data Science

Introduces the methodology of manipulating and extracting information from large data sets. Emphasizes the preliminary use of industry-standard software to explore and visualize patterns in data, as well as the ethical issues involved with collecting and interpreting real world data. Students will get hands-on practice at locating, wrangling and visualizing data to find patterns and uncover meaning. This course will meet General Education requirements. Prerequisite: Satisfaction of Math Preparatory Requirement.

Hands-on approach to methods of machine learning for data science, including support vector machines, dimensionality reduction and principal component analysis, unsupervised learning with K-means clustering, and artificial neural networks. Methods of model selection and evaluation. Applications are explored, some of which will be implemented using industry-standard libraries. Prequisites: MAT-280 and CIS-172