This course examines three major categories of topics in relation to data analysis and visualization. First, this course will cover the basic ways that data can be obtained from various sources, such as raw text files, web APIs, and data repositories. It will also cover the techniques of data cleaning and how to organize data for analysis. Second, the course will cover the essential techniques for analyzing quantitative data. It will teach prediction and clustering methods that are useful to solve various real data analysis tasks. In addition, students will learn major theories and recent methods in text analysis. Third, this course teaches how to create visualizations that effectively communicate the meanings behind data and information. The course will cover key practical skills in information visualization, such as plotting, mapping, and network visualization. This course will not be mathematically intensive. Instead, the course will use existing computational tools and programming libraries to solve various problems. You will use the R language and environment intensively for data analysis and visualization. Previously an LIS 690 course, not offered yet. Fulfills IT requirment.

Same as LIS 662: counts toward the Data Science core requirement for ICT master's students.