The world is awash with data and much more is on the way, creating a tidal wave of Big Data. Data Engineers develop the infrastructure to store, manage, analyse this wave of data, to bridge the gap between Data and Computer Science. This course will give you the skills you’ll need to succeed as a Data Engineer or Scientist from handling Big Data sources through to analysing and visualising this data to give meaningful insights. Subjects include:
- Big Data Theory and Practice
- NoSql Databases including Cassandra, Neo4j etc
- Parallel data analysis (Hadoop, Spark)
- Machine learning and data mining
- Languages for Data Engineering (Python, R, Matlab etc)
Why study this course at Dundee?
The role of "Data Scientist" has been described as the "sexiest job of the 21st Century." However, there is a emerging a new role, that of Data Engineer as more companies are realising they need employees with specific skills to handle the amount of data that is being generated and the coming tidal wave from the Internet of Things.
The Data Lab and the Scottish Funding Council (SFC) are funding 17 places on this course
Scottish or EU fee category students are eligible to apply to have their tuition fees paid in full. See SFC Funding Programmes for more details
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.
What is so good about this course?
You will have 24-hour access to our award winning and purpose-built Queen Mother Building. It 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 School of Computing 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.
The institution's focus on research was highly beneficial throughout the course, as it ensures that students get insight not only into well-known areas of computer science but into newly emerging studies which will shape tomorrow's world.
This course is taught by staff in the School of Computing .
The start date is October and the course lasts for 12 months on a full time basis.
The course will be taught by staff of the School of Computing. Depending on the modules you take this will include Andy Cobley, Dr keith Edwards, Professor Mark Whitehorn, and Professor Stephen McKenna, Professor Manuel Trucco, Professor Chris reed and Dr Jianguo Zhang.
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.
- Introduction to Data Mining and Machine Learning part 1
- Programming Languages for Data Engineering part 1
- Big Data
- Computer Vision
Options (dependant on experience)
- Transactional databases
- Secure Internet programming (including DevOps)
- 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
- 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
The course is assessed through a combination of examinations, coursework, presentations and interviews. 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%.
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
Year of Entry: 2015
Entry Requirements: 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.
EU and International students visit our EU and International webpages for entry requirements tailored to your home country.
English Language Requirement
IELTS of 6 (or equivalent), if your first language is not English.
Please check our English language requirements page for details of equivalent grades from other test providers, and information about the University of Dundee English language courses.
English Language Pre-Sessional Programmes
We offer Pre-Sessional programmes throughout the year and Foundation Programme(s) for both undergraduate and postgraduate students which start at the beginning of the academic year. These programmes are all 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. Successful completion of these programmes guarantees progression to various degrees at the University of Dundee as long as you hold a relevant offer.
The 30 week (one Academic Year) Foundation Programme(s) allow applicants who have not met our typical academic entry requirements, and require additional English Language support by up to 1.0 IELTS, to gain the necessary qualifications to enter the University of Dundee degree programmes in the following year.
The 24 week Pre-Sessional programme (March – August) provides additional English Language tuition for students who do not meet our minimum English Language requirements by up to 1.0 IELTS and the 10 week Pre-Sessional programme (June – August) (October – December) provides specialist English Language tuition for students who are 0.5 IELTS below the requirement for their degree programme.
The Data Lab, Scottish Funding Council (SFC), will fund 40 places across the 3 participating universities during the 2015/16 academic year. The University of Dundee has been awarded 17 of these funded places. If you are a Scottish or EU student*, you will be eligible to have your tuition fees paid in full through this scheme,
* You maybe eligible to apply for a place if you have been living in Scotland for a continuous period of 3 years prior to 1 August 2015. Please not that this does not include residency for the purposes of study only. You will not be eligible if you are not free of Immigration status.
Please indicate your interest in being considered for a funded place when you apply through UKPASS.
There have been many changes to the arrangements for funding students entering higher education in recent years, yet a degree from the University of Dundee, with its high rate of employment success, remains a cost-effective option.
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. Find out more about fee status.
|Fee category||Fees for students starting 2015/16|
|Scottish students||£4,500 per year of study
Applicants for this course may be eligible for a SFC fully-funded place
|Rest of UK students||£4,500 per year of study
See our scholarships for UK/EU applicants
|EU students||£4,500 per year of study
Applicants for this course may be eligible for a SFC fully-funded place
|Overseas students (non-EU)||£12,950 per year of study
See our scholarships for international applicants
Please read the Postgraduate "How to Apply" section for information on how to upload relevant documents to UKPASS before proceeding with your application.
Mr Andrew Cobley
School of Computing
+44 (0)1382 385078