ITI5202 · Data processing for big data

Overview

This unit focuses on big data processing, including volume, complexity, and velocity using the latest big data technologies. In big data volume, it covers large volume data processing using parallel technologies. In large dimensionality (or complexity), it covers various data analytics methods for parallel processing. For the velocity, it covers data streaming processing.

Offerings

Assessment

The Handbook lists an examination for this unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. identify and explain big data concepts and technologies;
2. write and interpret parallel database processing algorithms and methods;
3. apply common data analytics and machine learning algorithms in a big data environment;
4. use and evaluate streaming methods in big data processing;
5. use big data streaming technologies.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per teaching period typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.

Student experience, not official rules. Nothing here changes what the Handbook says.