Unless otherwise indicated, a grade of C or higher is required for all prerequisite
Introduction to the field of Big Data, its concepts and technologies, as well as R
programming. Students will explore the roles of a data scientist in terms of network
architecture, data analytics and predictive analysis. Fundamental questions of data
science and scenarios appropriate for each will be discussed. Differentiation among
raw data, clean data, and tidy data; and tools to convert data to/from these formats
will be covered. Effective management of large data in single and distributed computing
environments, including managing data redundancy and failures, will be covered. Testing,
correlation, clustering, and data visualization will be introduced. Intended for
students with previous programming experience.
Grade Option (Letter Grade or Pass/No Pass)
Lecture hours/semester: 48-54
Lab hours/semester: 48-54
Homework hours/semester: 96-108
Recommended: Eligibility for ENGL 838 or ENGL 848 or ESL 400. Completion of CIS 254.