Data Science - Data Visual & Acquisition

Catalog description

Being a data scientist requires an integrated skill set that spans the domains of statistics, machine learning, and computer programming. It also demands a solid foundation in the principles of data visualization in order to create effective data presentations that convey the intended message. Put simply, data visualization describes any effort to assist an individual's understanding of the significance of data by placing it in a visual context. In this course, students will be introduced to principles of effective data visualization and tools commonly used for its implementation. Techniques and strategies for visualizing different types of data (e.g., numeric data, non-numeric data, spatial-temporal data, etc.), the use of space and color to visually encode data, interactive visualizations, acquiring and visualizing data from publicly available data repositories, data cleaning and standardizing, are examples of some of the topics this course will address. The focus in the treatment of these topics will be on breadth, rather than depth, and emphasis will be placed on integration and synthesis of concepts and their application to solving problems. Prerequisite: While there are no formal prerequisites for this course, students should have a basic familiarity with the R statistical programming language (STAT 823 highly recommended). Prior experience using statistical software (e.g., R) to generate plots, tables, graphs, etc. is helpful, but is not required.