Assignment:
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.
Provide a reflection of at least 500 words (or 2 pages double spaced) of how the knowledge, skills, or theories of this course have been applied, or could be applied, in a practical manner to your current work environment. If you are not currently working, share times when you have or could observe these theories and knowledge could be applied to an employment opportunity in your field of study.