Skip to navigation | Skip to main content | Skip to footer
Menu
Menu

COMP60711 Data Engineering syllabus 2017-2018

COMP60711 Data Engineering

Level 6
Credits: 15
Enrolled students: 100

Course leader: John Keane


Additional staff: view all staff

Assessment methods

  • 60% Written exam
  • 40% Coursework
Timetable
SemesterEventLocationDayTimeGroup
Sem 1 P1 Lecture 2.19 Tue 09:00 - 12:00 -
Sem 1 P1 Lab 2.25 (A+B) Tue 13:00 - 17:00 -
Themes to which this unit belongs
  • Data Engineering and Systems Governance

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

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.
  • Lab assessment
  • Examination
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

Reading list

COMP60711 does not have a specified reading list.

Additional notes

Course unit materials

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