Associate Lecturer - CSCI316 Big Data Mining Techniques and Implementation (HE8)

Subject description

The subject considers the problems related to data mining techniques and implementation in Big Data environment. The topics include data pre-processing techniques, pattern, association and correlation discovery, classification and clustering, stream and real-time processing techniques and post-processing techniques like outlier detection, statistical and, proximity, and clustering base approaches. Laboratory classes and hands-on programming exercises related to these topics will provide the students with the abilities to design and implement Big Data algorithms and to use already existing software libraries. The subject also addresses the problems of scalability, selection of appropriate implementation techniques, and performance aspects when mining Big Data.

Job Requirement
- A Ph.D or Master's Degree in related discipline from a reputable university
- At least 2 years of relevant teaching experience at the tertiary level is preferred
- 5 years of relevant work experience will be an added advantage
- Applicant must be available to teach day classes or night classes or both day and night classes
- Supporting year 3 tutorials and lab

We regret that only shortlisted candidates will be notified.