Data Analytics - GC

Computing & Information Science studies the design, application, use, impact, and ethical implications of computational principles and technology. This foundational class prepares students for advanced courses in Data Sciences, Cyber Secutity, and Cyber Risk Management. Topics covered include: computer organization, data structures, computer programming, networking concepts, computer security, and ethics.

This course introduces students to concepts in probability and statistics including sampling distributions, normal theory estimation and hypothesis testing, regression and correlation, exploratory data analysis, logistic regression, discriminant analysis, resampling methods and linear model selection. Learning to do statistical analysis on a personal computer is an integral part of the course.

This course introduces students to fundamental concepts of computer programming. Topics include: algorithms, abstract data types, linear and non-linear data structures, and software engineering. Students will get hands-on experience with the use of a high level programming language to search and sort data.

This course is an overview of Hadoop, MapReduce, and Hadoop Tools. Considerable attention will be given first to Hadoop installation, both on the desktop and in the cloud. Students will learn how to navigate the Hadoop Distributed File System, and they will develop understanding of the MapReduce algorithm. Practice in writing MapReduce programs will be provided. The second half of the course will be devoted to essential Hadoop tools, including Pig, Hive, Flume, Sqoop, and Hbase. Programming experience is a prerequisite, and experience with Java and Unix will be helpful.