Study Data Engineering to learn how to store, manage and analyse data; bridging the gap between Data and Computer Science with this Big Data MSc.
This course will give you the skills you’ll need to succeed as a Data Engineer or Data Scientist, taking you from techniques to handle Big Data through to analysing and visualising this data to give meaningful insights. Subjects include:
- Big Data Theory and Practice, how do we manage the volume, velocity and variety of big data.
- NoSql Databases including Cassandra, Neo4j, Mongodb and a host of others.
- Parallel data analysis (Hadoop, Spark)
- Machine learning and data mining
- Languages for Data Engineering (Python, R, Matlab etc)
- Devops and Microservices for deploying Big Data solutions to the cloud.
The role of "Data Scientist" has been described as the "sexiest job of the 21st Century”, but it is the emerging role of Data Engineer that is set to have the biggest impact on companies data analytics. Companies are realising they need employees with a specific set of skills to bridge the gap between the work of Data Scientists and the infrastructure needed for a reliable and scalable solution. The Data Engineer needs to understand the work of Data Scientists at the same level of detail and take it to the next step, a working solution that will deliver day in and day out.
This MSc has been created with industry input to prepare its students with the skills to handle this wave of data and to be at the forefront of its exploitation. Students on the sister programmes ("Data Science" and "Business Intelligence") have gone on to work for some of the biggest companies in the industry and we are confident that graduates from this MSc will have the same success.
The University of Dundee has been successfully offering related MSc programmes such as Business Intelligence and Data Science since 2010. These innovative programmes attract around 40 students per year, drawn from across Europe and Overseas.
The University of Dundee is part of The Data Lab which is an Innovation Centre with the aim of developing the data science talent and skills required by industry in Scotland. The Data Lab MSc is a collaborative effort between Robert Gordon University, The University of Dundee, The University of Stirling and The Data Lab, with the aim of developing the data science talent and skills required by industry in Scotland. Through this collaboration, each university has developed an MSc program based on its own strengths and capabilities, with the support of The Data Lab to facilitate industry involvement and collaboration, and provide funding and resources.
The Queen Mother Building has an unusual mixture of lab space and breakout areas, with a range of conventional and special equipment for you to use. Our close ties to industry allows us access to facilities such as Windows Azure and Teradata University along with industry standard software such as Tableau and the SQL server stack for you to evaluate and use. Our experience with open source big data tools such as Cassandra, Hadoop, Spark and Storm will help you get started in building your own cluster, either physical or in the cloud.
The university maintains a friendly, intimate and supportive atmosphere, and we take pride in the fact that we know all of our students. We have a thriving postgraduate department with regular seminars and guest speakers. As part of the Data Lab program, you will be invited to specially arranged industry talks across Scotland.
The University of Dundee has close ties with the Big Data industry, including Teradata, Datastax and Microsoft. We have worked with SAS, Outplay, Tag, GFI Max, BrightSolid and BIPB, and our students have enjoyed guest lectures from Big Data users such as O2, Sainsbury’s, M&S and IBM.
You will be able to work with a range of leading researchers and tutors, including vision and imaging researchers, Business Intelligence experts along with industrial and research data scientists.
Teaching Excellence Framework (TEF)
The University of Dundee has been given a Gold award – the highest possible rating – in the 2017 Teaching Excellence Framework (TEF).
The start date is October and the course lasts for 12 months on a full time basis.
How you will be taught
The course will be taught by staff of Computing.Depending on the modules you take this will include
- Andy Cobley
- Dr Keith Edwards
- Professor Mark Whitehorn
- Professor Stephen McKenna
- Professor Manuel Trucco
- Professor Chris Reed
- Dr Jianguo Zhang.
How you will be assessed
The course is assessed through a combination of
Each module is different: for instance the Big data module has 40% coursework, consisting of Erlang programming and a presentation on nosql databases, along with an examination worth 60%.
What you will study
The course will be taught in 20 credit modules with a 60 credit dissertation. Students will require to complete 180 credits for the award of the MSc (including 60 credits for the dissertation). Students completing 120 credits (without the dissertation) will be eligible for a Postgraduate Diploma.
Detailed module information is available online.
- Introduction to Data Mining and Machine Learning part 1
- Programming Languages for Data Engineering part 1
Options (dependent on experience)
- Big Data
- Computer Vision
- Devops and Microservices
- Secure Internet programming
- Technology Innovation Management
- Introduction to Data Mining and Machine Learning part 2
- Programming Languages for Data Engineering part 2
- Business Intelligence Systems
- Research Methods
- Research project with optional industrial collaboration
Each module on the course is designed to give the student the skills and understanding they need to succeed in the Data Engineering/ Science field. Content on the course includes (but is not limited to):
- CAP theorem
- Lamda Architecture
- Cassandra, Neo4j and other nosql databases
- The Storm distributed real time computation system
- Hadoop, HDFS, MapReduce, and other Hadoop/SQL technologies
- Spark framework
- Data Engineering languages such as Python, erlang, R, Matlab
- Continuous deployment and delivery to the cloud.
- Microservices using containers to deliver reliable infrastructure.
- Vision systems, which are becoming increasingly important in data engineering for extracting features from large quantities of images such as from traffic, medical and industrial
- RDBMS systems which will continue to play an important role in data handing and storage. You will be expected to research the history of RDMBS and delve in to the internals of modern systems
- OLAP cubes and Business Intelligence systems, which can be the best and quickest way to extract information from data stores
- Goals of machine learning and data mining
- Clustering: K-means, mixture models, hierarchical
- Dimensionality reduction and visualisation
- Inference: Bayes, MCMC
- Perceptrons, logistic regression, neural networks
- Max-margin methods (SVMs)
- Mining association rules
- Bayesian networks
Our experience suggests that graduates of this course will have most impact in the following areas:
- Cloud and web based industries that handle large volumes of fast moving data that need to be stored, analysed and maintained. Examples include the publishing industry (paper, TV and internet), messaging services, data aggregators and advertising services
- Internet of Things. A large amount of data is being generated by devices (robotic assembly lines, home power management, sensors etc.) all of which needs to be stored and analysed.
- Health. The NHS (and others) are starting to store and analyse patient data on an unprecedented scale. The healthcare industry is also combining data sources from a large number of databases to improve patient well-being and health outcomes
- Games industry. The games industry records an extraordinary amount of data about its customers' play activities, all of which needs to be stored and analysed. This course will equip students with the knowledge and skill to engage with the industry
Applicants should normally have an Honours degree at 2.1 level or above in computing or a related subject. All prospective students need to undergo a technical interview to ensure they have the necessary background knowledge, including Mathematics, to undertake the course.
English Language Requirement
English Language Programmes
We offer Pre-Sessional and Foundation Programme(s) throughout the year. These are designed to prepare you for university study in the UK when you have not yet met the language requirements for direct entry onto a degree programme.
The fees you pay will depend on your fee status. Your fee status is determined by us using the information you provide on your application.
|Fee status||Fees for students starting academic year 2019-20|
|Scottish and EU students||£7,950 per year of study
See our scholarships for UK/EU applicants
|Rest of UK students||£7,950 per year of study
See our scholarships for UK/EU applicants
|Overseas students (non-EU)||£20,950 per year of study
See our scholarships for International applicants
You may incur additional costs in the course of your education at the University over and above tuition fees in an academic year.
Examples of additional costs:
|One off cost||Ongoing cost||Incidental cost|
|Graduation fee||Studio fee||Field trips|
*these are examples only and are not exhaustive.
- may be mandatory or optional expenses
- may be one off, ongoing or incidental charges and certain costs may be payable annually for each year of your programme of study
- vary depending on your programme of study
- are payable by you and are non-refundable and non-transferable
Unfortunately, failure to pay additional costs may result in limitations on your student experience.
For additional costs specific to your course please speak to our Enquiry Team.
You apply for this course through our Direct Application System, which is free of charge. You can find out more information about making your application when you click Apply Now below
|Apply now||Data Engineering MSc||P052247|
Mr Andrew Cobley
Science and Engineering
+44 (0)1382 385078