Data Management and Engineering MGT5492

  • Academic Session: 2025-26
  • School: Adam Smith Business School
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 1
  • Available to Visiting Students: No
  • Collaborative Online International Learning: No
  • Curriculum For Life: No

Short Description

This course equips students with essential skills in data management and engineering for business analytics. Through lectures and hands-on labs, students explore SQL programming, data modelling, database design, and emerging technologies like big data and blockchain. Emphasis is also placed on ethical data handling, security, and privacy.

Timetable

Campus Lectures: 16 hours (8 x 2 hours)

Campus Lab workshops: 10 hours (5 x 2 hours)

Requirements of Entry

Please refer to the current postgraduate prospectus at: http://www.gla.ac.uk/postgraduate/

Excluded Courses

None

Co-requisites

None

Assessment

ILOs

Assessment

Weighting

Length/Duration

1, 3 & 4

Individual Assignment

25%

1000 words

Course Aims

This course is designed to provide students with a comprehensive understanding of the principles and practices of data management and engineering, with a focus on their application in business analytics. It aims to foster analytical thinking in managing data systems, promote awareness of ethical and regulatory considerations, and introduce emerging technologies such as big data and blockchain. The course prepares students to design and evaluate data solutions that address complex challenges in modern business environments.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

1. Analyse and apply data management frameworks to propose and justify solutions for complex business analytics problems using case-based assessments or project work.

2. Apply advanced SQL techniques to design, query, and optimize relational databases, ensuring efficient storage, retrieval, and management of data.

3. Critically evaluate database architectures and data models, identifying and resolving issues related to normalization, data integration, and system performance.

4. Develop and defend strategies to address ethical and regulatory considerations, including data security and privacy requirements, in designing data systems.

5. Design and implement robust database systems by integrating advanced features and security measures to address business-specific challenges.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.