School of Computing

Department of Computer Science

CS5344:   Big Data Analytics Technology  

 


[Announcements]  [Instructor] [Course Objectives] [Lecture Schedule] [Reference Texts and Materials] [Assignment (Hadoop Labs & Gradiance] [Project] [Assessment]


Objectives

This module looks at technologies for big data analytics. The focus will be on the “technologies”, i.e., the tools/algorithms that are available for a variety of “analytics”. The data may be so BIG that they do not fit into the main memory. As an example, customer segmentation (which is the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, spending habits, etc) allows a company to target specific groups of customers effectively and allocate marketing resources to best effect. The “tool” that we can use is “clustering”. As another example, online retailer such as Amazon uses recommendation technologies extensively to present information items (movies, music, books, etc) that are likely of interest to the user. One such approach is to “identify pairs of items that, while they might not be bought by many customers, had a significant fraction of their customers in common”. This will allow online retailers to identify potential customers, e.g., “if this user is looking at or has purchased the book “Big Data Analytics” then it makes sense to advertise this other book “Big Data Technology” (perhaps even as a sales item) because there are many other users who have bought both books”.

 

Learning Outcomes

At the end of the module, students will possess the skills necessary for utilizing tools (including deploying them on Hadoop/MapReduce) to handle a variety of big data analytics, and to be able to apply the analytics techniques on a variety of applications.