Course Objectives

Main Objectives

The main objective of this course is to help students learn, understand, and practice big data analytics and machine learning approaches so that they will be able to contribute to big data science. On successful completion of this course the students will have skills and knowledge to

  • connceptualize and summarize big data and machine learning problems and solutions that the industry and organizations encounter;

  • recognize and extract the unique characteristics of trivial data and big data, and distinguish them for cost effectiveness;

  • explore and select emerging big data computing technologies that are suitable for their industry applications ;

  • able to select and process machine learning techniques efficiently for different big data applications;

  • handle efficiently and effectively the scaling up problems and challenges associated with machine learning approaches.

These objectives will be achieved by adopting the teaching and learning methodologies and principles proposed by Dr. Suthaharan in his recent paper published by ACM, which is listed below.


It stands for Flexible Learning and Sequential Knowledge Update and it has been developed and applied to a computer networking course at the department of computer science (UNCG).

Shan Suthaharan. "FLaSKU-a classroom experience with teaching computer networking: is it useful to others in the field?." Proceedings of the 15th Annual Conference on Information Technology Education. ACM, 2014.

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