MSc in Data Engineering

Why study Data Engineering 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.

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 School of Computing at 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.

What's so good about Data Engineering at Dundee?

Our facilities

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. It's also easy to work on your own laptop as there is wireless access throughout the building. Our close ties to industry allows us access to facilities such as Windows Azure and Teradata, and university and industry standard software such as Tableau for you to evaluate and use.

Postgraduate culture

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 - you're far more than just a matriculation number to us. We have a thriving postgraduate department with regular seminars and guest speakers.

Special features

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 top vision and imaging researchers and BI experts. Our honorary staff include legal experts, entrepreneurs and renowned industry experts such as John Richards of the newly formed IBM Watson Group.

Who should study this course?

We are looking for students with:

  • Insatiable curiosity
  • Interdisciplinary interests
  • Excellent communication skills

This course suits recent graduates in Computer Science (or related subjects) who wish to focus on data wrangling and the application of data analytics. Students should have a good grasp of Mathematics and a basic understanding of programming. Students with a scientific background will be considered if they can show that they have a relevant maths and computer background.

Modules

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.

Semester 1
  • Introduction to Data Mining and Machine Learning part 1
  • Programming Languages for Data Engineering part 1
  • Big Data
  • Computer Vision
Semester 2
  • Introduction to Data Mining and Machine Learning part 2
  • Programming Languages for Data Engineering part 2
  • Business Intelligence Systems

Options:

  • Transactional Database Systems
  • Research Methods
Semester 3
  • Research project with optional industrial collaboration
Course content

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 and Shark frameworks
  • 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 will be taught by staff of the School of Computing. Depending on the modules you take this will include Andy Cobley, Professor Mark Whitehorn, and Professor Stephen McKenna.

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%.

Entry Requirements

Entry Requirements

EU and International students visit our EU and International webpages for entry requirements tailored to your home country.

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

IELTS of 6.0 (or equivalent), if your first language is not English. Please check our 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.


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