MSc Data Engineering

This module is only available for students enrolled on to MSc Data Engineering.


This module covers core database techniques including relational including relational and NoSQL databases. The module will build on the School of Computing’s expertise in online database delivery (such as the SQA recommended SQL Zoo) to ensure the students understand how to store and retrieve data using several different tools. Database architectures, functionality, and entity-relationship modelling will be covered. The role of a Database Administration in the context of data science will be analysed. Finally, current trends in database technology will be explored. Topics include:
• Database theory.
• Database design.
• Database architecture and functionality.
• Date analysis and entity-relationship modelling.
• Normalisation for database design.
• SQL and relational algebra.
• NoSQL databases.
• Role of the database administrator.
• Current trends in database technology.
• Database security.

This module is NOT available unless you have successfully completed 120 Credits.
MSc Dissertation

This module is only available for students enrolled on to MSc Data Engineering.

This module will explore and develop data management and processing solutions that will work on dirty, complex, real-world data. This module will examine the key concepts of data warehousing, data cleaning, and data processing in the context of business requirements and focus on how to combine these steps into a coherent data processing pipeline.
First, modern tools and techniques in data management will be examined, with the emphasis on good practice and professional approaches of storing and handling data. Next, the module will examine ways of cleaning noisy real-world data in order to make it suitable for data processing. Finally, data processing and collation techniques such as Machine or Deep Learning will be applied to the data to extract structure and elicit comprehension of the data. Throughout the module, advantages and disadvantages of using local and cloud approaches will be explored, alongside discussing common parallel approaches to facilitate faster solutions.
In short, the goal of this module is to allow students to understand a data processing pipeline from raw data to final delivery. It will cover:
• Data warehousing and storage techniques
• Data cleaning techniques
• A discussion of cloud approaches
• Data processing and collation techniques
• An introduction to parallel data pipeline approaches