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Current postgraduate taught students

COMP60721: Data Engineering (2011-2012)

This is an archived syllabus from 2011-2012

Data Engineering
Level: 6
Credit rating: 15
Pre-requisites: No Pre-requisites
Co-requisites: No Co-requisites
Lecturers: John Keane, Sandra Sampaio
Course lecturers: John Keane

Sandra Sampaio

Additional staff: view all staff
Sem 1 P2 Lecture 2.19 Mon 09:00 - 12:00 -
Sem 1 P2 Lab 2.25abcd Mon 13:00 - 17:00 -
Assessment Breakdown
Exam: 50%
Coursework: 50%
Lab: 0%

Themes to which this unit belongs
  • Data Management


All application areas are witnessing the "data deluge", i.e. the ever growing amount of digital data available as part of day-to-day activities in business, science, education, entertainment, etc. Engineering and managing such data is a key for success of any organisation. In addition to the need to work with huge volumes of data, current applications are also challenged with multi-modal data, including un- and semi-structured data, image and video data, spatial and temporal data, etc.


This module will examine the entire data life cycle, including data creation, modelling, acquisition, representation, use, maintenance, preservation and disposal. As the majority of data is stored in databases, the module will examine various database engineering approaches to support data management, including database design, data warehousing, maintenance and analytics. Data standards and data quality will be also examined.

Programme outcomeUnit learning outcomesAssessment
A1Have an understanding of the data life cycle.
  • Examination
A2 C4 D4Have an understanding of data engineering techniques, and be able to perform and document large-scale data engineering for a given task, comprising various multimodal data types.
  • Examination
  • Lab assessment
A2Have an understanding of technical, ethical and societal issues related to data engineering, storage, access and maintenance.
  • Examination
A2 B1 B3 C4Understand the main principles of data analytics and explain how it can be used in various application areas.
  • Examination
A2 B1 D3 D4Demonstrate an understanding of relevant standards and best practice in data engineering and be able to identify, understand and articulate the shortcomings of existing data engineering practices and to suggest, in broad terms, possible strategies and approaches that might be used to overcome them.
  • Examination
  • Lab assessment


An overview of the data life cycle
Data engineering, modelling and design techniques
Data storage and warehousing
Data access and maintenance
Data analytics and visualisation
Engineering non-traditional data types
Data standards and data quality