Data Analytics and Big Data
This course introduces the student to concepts of big data management, database management, data mining techniques and the underlying statistics that support big data analytics and uses the programming language R as the primary tool for analysis.
Abstract
Course Description CSCE 587 – Big Data Analytics (3) Prereq: STAT 509 or STAT 515 or STAT 513. Foundational techniques and tools required for data science and big data analytics. Concepts, principles, and techniques applicable to any technology and industry for establishing a baseline that can be enhanced by future study. Course Overview This course introduces the student to concepts of big data management, database management, data mining techniques and the underlying statistics that support big data analytics. In this course we will use the programming language R as the primary tool for analysis. Learning Outcomes By the end of the course the student will be able to: 1. deploy a structured lifecycle approach to data science and big data analytics projects 2. select visualization techniques and tools to analyze big data and create statistical models 3. use tools such as R and RStudio, and MapReduce/Hadoop. This course does not have a required text. However, ad hoc readings from the field will be assigned. In addition, material from " Data Science and Big Data Analytics Student Guide " distributed by EMC Education Services will be provided to the students. Course Delivery Structure The course will be delivered in a computer-equipped classroom. Approximately 50% of the time will be devoted to lecture and the other 50% devoted to the supervised working through of exercises. Course Requirements Readings: Students will read lecture material assigned for each class prior to the class. Homework: Students will complete assignments demonstrating mastery of material. These will be due at the beginning of class.