INSY 4970: Analytics and Visualization of Big Data

Course Description (Spring 13, 53 Students) -- Sponsored by an Amazon Web Services Education Coursework Grant

As our information infrastructure evolves, our ability to store, extract, and analyze data is rapidly changing. Big data is a popular term that is used to describe the large, diverse, complex and/or longitudinal datasets generated from a variety of instruments, sensors and/or computer-based transactions. The term big data refers not only to the size or volume of data, but also to the variety of data and the velocity or speed of data accrual. As the volume, variety, and velocity of data increase, our existing analytical methodologies are stretched to new limits. These changes pose new opportunities for engineering practitioners and researchers. In this course, we will explore how big data can be extracted and analyzed to discover new information that complements our existing knowledge of the system being studied. This will include a discussion of suitable algorithms for high dimensional data, graphs, data from multiple data streams, machine learning, as well as several applications that include mining Twitter data, and recommender systems for web advertising. At the end of the course, you will be prepared to utilize web data to generate valuable information about the state of any production/service system. More importantly, you will be able to utilize unstructured customer information from the web in developing more complete models about any system. The insights gained from such models can transform the operations at several companies.

Sample Student Project from Spring 13

One student was interested in understanding the impact of population demographics and economy on the number of students coming to study in the U.S. with a student visa. To understand such impacts, he developed a visual analytics model that allows for visualizing the effect of GDP per Capita and population on the number of student visas per country. His model can be found at this link and a more detailed description of his approach can be found at the Class Blog.

Additional Information on the Course

For more details on the course, please refer to the Syllabus