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This is an archived syllabus from 2013-2014

COMP60711 Data Engineering syllabus 2013-2014

COMP60711 Data Engineering

Level 6
Credits: 15
Enrolled students: 75

Course leader: John Keane

Additional staff: view all staff

Assessment methods

  • 50% Written exam
  • 50% Coursework
Sem 1 P1 Lecture 2.19 Tue 09:00 - 09:00 -
Sem 1 P1 Lab 2.25abcd Tue 13:00 - 13:00 -
Themes to which this unit belongs
  • Data Engineering and IT Governance
  • Managing Data


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. Indeed "Big data" has become part of modern vernacular. Engineering, managing and analysing such data is a key for success of all organisations. 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 examined and the challenge of "big datasets" will be considered.


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

Feedback methods

Regular coursework, returned marked with feedback

Study hours

Employability skills

  • Analytical skills
  • Problem solving
  • Research
  • Written communication

Learning outcomes

On successful completion of this unit, a student will be able to:

Learning outcomes are detailed on the COMP60711 course unit syllabus page on the School of Computer Science's website for current students.

Reading list

Data mining : concepts and techniques Han, Jiawei.9780123814807; 9780123814791Elsevier©2012.
Data mining : practical machine learning tools and techniques Witten, I. H.0128043571; 9780128043578Morgan Kaufmann Publisher[2017]
Artificial intelligence : a textbook Aggarwal, Charu C.,9783030723576; 3030723577Springer[2021]
Introduction to data mining Tan, Pang-Ning,9780273775324; 0273775324Pearson Education Limited2019.
Measuring data quality for ongoing improvement : a data quality assessment framework Sebastian-Coleman, Laura.9780123977540; 0123977541Elsevier Science2013.
The Philosophy of Information Quality undefined9783319071213Springer International Publishing ; Imprint Springer2014.
Information quality in information fusion and decision making undefined303003643X; 9783030036447; 3030036448; 9783030036430Springer[2019]

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.