Data Engineering MSc

Semester 1

• 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

Semester 2

• Introduction to Data Mining and Machine Learning part 2
• Programming Languages for Data Engineering part 2
• Business Intelligence 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 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

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.

The course is assessed through a combination of

• examinations
• coursework
• presentations
• 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%.