MSc ACS: Digital Biology / Course details

Year of entry: 2024

Course unit details:
Data Engineering

Course unit fact file
Unit code COMP60711
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Available as a free choice unit? Yes


The Harvard Business Review in October 2012 described the role of data scientist as 'the sexiest job of the 21st century'. The 'big data' phenomenon has become part of the vernacular, with the digital universe expected to grow by a factor of 44 from 2009-2020 to a trillion Gigabytes [IDC Digital Universe Study, 2010]. This has led to recognition of a data lifecycle and the need for its systematic management, including both technical and societal issues. Particular focus here is on issues such as data standardisation and data quality, and data analytics (description and prediction) across all application domains.


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.

Learning outcomes

  • Explain and apply the constituent steps of the data life cycle

  • Describe data engineering techniques; be able to apply and document large-scale data engineering for a given task, comprising various multimodal data types.

  • Describe and apply technical, ethical and societal issues related to data engineering, storage, access and maintenance.

  • Explain and apply the main principles of data analytics/ algorithms, and explain their application to various domains.

  • Describe relevant standards and best practice in data engineering, analyse shortcomings and identify possible strategies and approaches to overcome them.


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

Employability skills

Analytical skills
Problem solving
Written communication

Assessment methods

Method Weight
Written assignment (inc essay) 100%

Feedback methods

Regular coursework, returned marked with feedback

Study hours

Scheduled activity hours
Lectures 10
Practical classes & workshops 20
Independent study hours
Independent study 120

Teaching staff

Staff member Role
Sandra Sampaio Unit coordinator

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