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Catalog Course Description

In this course, students explore key data analysis and management techniques, which applied to massive datasets are the cornerstone that enables real-time decision making in distributed environments, business intelligence in the Web, and scientific discovery at large scale. In particular, students examine the map-reduce parallel computing paradigm and associated technologies such as distributed file systems, non-sql databases, and stream computing engines. This highly interactive course is based on the problem-based learning philosophy. Students are expected to make use of technologies to design highly scalable systems that can process and analyze Big Data for a variety of scientific, social, and environmental challenges

Course Objectives

Upon completion of the course, students will be able to

• Explain Big Data Analytics, and its importance to today’s organizations.

• Understand the Big Data analytics lifecycle.

• Explore basic data analytic methods using R.

• Examine clustering analysis methods.

• Survey association rules.

• Show how to implement regression analytics.

• Employ classification analysis methods.

• Explore time series analysis methods.

• Understand text analysis.

• Survey analytics technology and tools.

• Examine in-database analysis techniques.

• Understand how to apply analysis techniques in real life situations.