This is an archived syllabus from 2013-2014

View 2022-2023 syllabus.

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

Timetable

Semester	Event	Location	Day	Time	Group
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

Overview

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.

Aims

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.

Syllabus

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

Title	Author	ISBN	Publisher	Year
Data mining : concepts and techniques	Han, Jiawei.	9780123814807; 9780123814791	Elsevier	©2012.
Data mining : practical machine learning tools and techniques	Witten, I. H.	0128043571; 9780128043578	Morgan Kaufmann Publisher	[2017]
Artificial intelligence : a textbook	Aggarwal, Charu C.,	9783030723576; 3030723577	Springer	[2021]
Introduction to data mining	Tan, Pang-Ning,	9780273775324; 0273775324	Pearson Education Limited	2019.
Measuring data quality for ongoing improvement : a data quality assessment framework	Sebastian-Coleman, Laura.	9780123977540; 0123977541	Elsevier Science	2013.
The Philosophy of Information Quality	undefined	9783319071213	Springer International Publishing ; Imprint Springer	2014.
Information quality in information fusion and decision making	undefined	303003643X; 9783030036447; 3030036448; 9783030036430	Springer	[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.