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Overview

The world is currently awash with massive volumes of data (Big Data) generated from all sectors of society including business, government, health, transport, web, and social networks (Facebook, Twitter). Data science and engineering has become the driving force behind critical decisions for all organizations by turning massive volumes of data into actionable information and knowledge. Data professionals with the skill set to comprehend, process and manage big data efficiently and effectively are already highly demanded. A recent Mckinsey Global Institute report forecasts a serious shortage of data science and engineering professionals in the near future.
Study Level

Postgraduate

Minimum Units of Credit

96

Specialisation Type

Specialisation

Specialisation Structure

Students must complete 96 UOC.

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Disciplinary Electives

Disciplinary Electives description.

Students must take 36 UOC of the following courses.
Note: With approval of program authority, students may choose up to two Level 4 or higher elective courses outside of the School of Computer Science and Engineering. These courses are listed under "Non-Computing Electives".

Advanced Discipinary Knowledge (ADK) Requirement

Advanced Discipinary Knowledge (ADK) Requirement description.

At least 36 UOC of Disciplinary Elective, Prescribed Elective and/or Non-Computing Elective courses chosen must be taken from the following Advanced Disciplinary Knowledge Course List.

Prescribed Elective Requirement

Students must choose at least two courses (12 UOC) from each of two out of the three disciplinary elective lists.
- Databases & Data Mining
- Machine Learning, Information Retrieval, Knowledge Representation
- Algorithms and Statistics

NON-COMPUTING ELECTIVES

NON-COMPUTING ELECTIVES description.

The following courses are outside the School of Computer Science and Engineering (CSE) and may be taken with approval from the program authority and within the maximum 12 UOC Level 4 or higher elective courses outside of the CSE rule.

Additional Information

Entry Requirement

Students entering the program should have a solid background in computing and mathematics and good analytical skills.

Pre-2019 Handbook Editions

Access past handbook editions (2018 and prior)

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