Course
This course will cover advanced topics in data mining on high dimensional data, including advanced feature selection techniques, advanced pattern mining, similarity searches (including minwise hashing and locality sensitive hashing), advanced classification methods, advanced cluster analysis, mining data streams, mining social networks, tree/graph mining, and privacy-preserving issues in data mining. Students are expected to have a course in data mining before taking this course.
Course
In this course students study advanced methods to handle and analyze very large data sets in Hadoop's Big Data environment. Students work with the Spark architecture in the MapReduce framework. Students also learn to apply machine learning algorithms in Spark.
Course
This course introduces students to the handling of Big Data on Hadoop's MapReduce environment. Students also learn Spark architecture and programming with the aim of doing big data analytics with machine learning algorithms in Spark.
Course
Major individual research in an area of significant educational interest; designed specifically for candidates in the Ed.D. Curriculum and Instruction program. This dissertation will reflect intensive educational research produced by the student and collaboratively developed with the student's graduate committee. Graded on a satisfactory/unsatisfactory basis only. Admission to candidacy and completion of all other doctoral program requirements are required.
Course
Introduction to database systems and database management system architectures. Various database models are discussed with emphasis on the relational model and relational database design. Case applications using fourth-generation languages, such as SQL are included. This course requires completion of graduate foundations courses in computer programming or the equivalent undergraduate coursework.