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Course Contents

In this course, students will develop their knowledge of big data analytics and enhance their programming and mathematical skills. They will learn to use essential analytic tools such as Hadoop, R and MOA (Massive Online Analysis). Topics covered in this course include: • cloud-based big data analysis; • predictive analytics, including probabilistic and statistical models; • application of large-scale data analysis; • analysis of problem space and data needs; • understanding of ethical and social concerns of data mining. By the end of this course, you will be able to approach large-scale data science problems with creativity and initiative

Course Synopsis

This course 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”.

Course Learning Outcomes

This course offers extensive training in big data technology and methods, providing the opportunity to upgrade existing skills to the state-of-the-art in areas such as Data Mining, Programming for Distributed Processing Systems, NoSQL Databases, Text Analytics techniques and leveraging Cloud Computing platforms for Big Data Analytics. A strong research focus will equip students with industry-based research for Big Data Analytics solutions.

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