MSc Data Engineering
Data Engineering is a major growth area within both the commercial and public sectors, and there is a recognised shortage of professionals that have the required range of Data Engineering knowledge and skills. This online MSc Data Engineering programme addresses this shortage.
The programme is aimed at graduates and practitioners with a background in business, quantitative science, or computing who wish to develop into effective Data Engineers with the business understandings and analytical, statistical and computing skills to contribute to this vital area for contemporary commercial and public sectors.
Edinburgh Napier University has excellent research and knowledge transfer links with many local, national and international organisations in Data Engineering related areas.
The acquisition of knowledge and skills on the programme will give students a critical understanding of the tools and technologies involved in the analysis, design, development, testing, evaluation and modification of Data Engineering solution; enable them to select and evaluate appropriate tools for the collection, processing and presentation of complex and diverse data sets, and critically review an organisation’s data needs and make appropriate and measurable recommendations on the use of Data Engineering techniques.
Delivered online (part-time), this MSc is ideally suited to individuals who intend to balance their personal and professional commitments with their studies. Course Details You will study the software engineering process in both creating and working with large and complex data sets, acquiring the knowledge and experience of tools and techniques that are necessary to be a successful Data Engineer in a range of environments. The focus of the programme is on the practical application of these skills and tools, with the underpinning theories used within this context.
Learning, teaching and assessment methods focus on providing students with engaging and contemporary materials that link theory to practice and require students to take a critical perspective on both.
The programme will benefit people wanting either to change career and start working in data solution development, or to upskill from being a software engineer or data scientist to the combined role of Data Engineer. As the programme is paced to suit the learner, the MSc in Data Engineering will also support learners coming back to education.
Your final dissertation project will allow you to use the tools and approaches you have developed during the course.
You can pay for this course flexibly on a module-by-module basis. This means that you don’t have to pay the full course cost upfront.
|Modules (ALL COMPULSORY)|
|Trimester 1 (15 weeks)||Trimester 2 (15 weeks)||Trimester 3 (15 weeks)|
|Data Management and Processing
|Business Intelligence and Reporting for Enterprises
|Data-Driven Decision Making
|Information Systems Engineering
|Trimesters 4 and 5 (15 weeks – 30 weeks)|
Data-Driven Decision MakingThe aim of the module is to enable you develop a deep understanding of the business context and impact of data, the meaning of the data (including in terms of statistics), and to give you an opportunity to express this in the form of professional written reports.
Topics covered include: The role of the data scientist; Data strategy and Key Performance Indicators (KPIs); Deployment and implementation; Governance, ethical and cultural implications; Exploring and describing data; Statistical inference – parametric methods t-tests and Analysis of Variance Statistical presentation of data; Multivariate methods – principal component analysis, exploratory factor analysis and segmentation methods (Hierarchical clustering, K means and K modes); Statistical modelling – OLS regression, general linear models exemplified by Binary Logistic models; Diagnosing model fits.
Database SystemsThis module covers core database techniques 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 understanding of 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 engineering will be analysed. Finally, current trends in database technology will be explored.
Topics include: Database theory; Database design; Database architecture and functionality; Data 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.
Business Intelligence and Reporting for EnterprisesThe module presents a balanced approach to the subject area by addressing both the theory and practice of Business Intelligence and reporting in enterprise systems. The aim of this module is to develop a deep understanding of enterprise information systems and their role in business processes alongside practical skills in business intelligence reporting and dashboard design.
It includes: Evaluation of the impact that Enterprise Information Systems can have within organisation business processes; An assessment of the impact that they have on the efficiency and effectiveness of organisations and supply chains; Fundamentals of business intelligence, such as data warehousing and data mining; The role of performance dashboards in performance management and measurement in organisations; The sourcing and extraction of data and the analysis and development of intelligence.
Data WranglingThis module introduces a range of tools and techniques necessary for working with data in a variety of formats with a view to developing data driven applications. The module focuses primarily on developing applications using the Python scripting language and associated libraries and will also introduce a range of associated data storage and processing technologies and techniques.
The module covers the following topics: Data types and formats: numerical and time series, graph, textual, unstructured; Data sources and interfaces: open data, APIs, social media, webbased; NoSQL databases such as document (MongoDB), graph and key value pair; Techniques for dealing with large data sets, including Map Reduce; Developing Data Driven Applications in Python.
Data Management and ProcessingThis 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 you 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.
Information Systems EngineeringThis module will explore and develop both the theory and practice of Information Systems Engineering.
Topics will include: Information Systems development life cycles and methodologies, with emphasis on Agile Development, Data Driven Systems Development, and Dev Ops; Requirements elicitation, prioritisation, modelling and specification; The Unified Modelling Language (UML), Object Oriented Development; Test Driven Design and Development; Security by Design, and Misuse case Modelling.
DissertationThe work for this module comprises the completion of an individual research project. You will be assigned a personal Supervisor, and an Internal Examiner who monitors progress and feedback, inputs advice, examines the dissertation and takes the lead at the viva. There are three preliminary deliverables prior to the submission of the final dissertation: (1) Project proposal (there is a formal approval process by module leader before projects start); (2) Initial Report; (3) Outline dissertation.
- Assessments are completed and submitted through the secure learning environment. The schedule for Assessments is published in advance. There are no formal exams – assessments is by end of unit quizzes and end of module assignments.
- The MSc Data Engineering comprises of six modules and a dissertation to a timescale that matches your requirements, but can be completed in 21-33 months minimum/4 years max.
- The entry requirement for this course is a Bachelor (Honours) Degree at a 2:2 or above in an appropriate field, for example, software development, computing, or business analytics. Alternatively, other qualifications or experience that demonstrate through our recognition of prior learning process that you have appropriate knowledge and skills may be considered.