Data Science MSc by Research

Data science is positioned to be the major new arena of scientific discovery in the 21st century. As Eric Schimdt, CEO of Google, has pointed out, we now generate as much information every two days as we did from the dawn of human civilisation up until the year 2003.

To cope with this vast amount of data, there is an urgent requirement to derive meaningful insights from very large and diverse data sources: the so-called 'big data challenge'. Secure identification of individuals, accurate financial prediction and reliable cancer diagnosis are all examples of area in which the technologies underpinning data science are marketing revolutionary contributions, enriching our lives, and making our future healthier, more efficient and more secure.

Why study MSc Data Science by Research at Middlesex University?

The MSc by Research (Data Science) provides an opportunity for students who wish undertake an individual real-world big-data research project; you can choose a project area from a wide variety of interdisciplinary domains, including business information systems, e-health, social media, cloud computing, smart homes, intelligent vehicles and ambient assisted living.

We currently have supervisors available in:

• Machine learning
• Visual analytics
• Mathematics and statistics
• Smart sensors (for ambient assisted living and smart homes)
• Numerical algorithms (especially computational geometry)
• Artificial intelligence
• Ethics of technology
• Psychology
• Business information systems and business informatics
• Robust software/system development
• Networking
• Ethics
• Marketing
• Operations management
• Enterprise and economic development
• Education
• Design

What will you study on the MSc Data Science by Research?

The MSc by Research (Data Science) aims to bring together, in an interdisciplinary fashion, data science-related activities from across the University, centring on the core activities of machine-learning, visual analytics and data storage. These will work together to support applications-driven data scenarios.

To support the interdisciplinary nature of this programme, you will have a core activity supervisor (machine learning, visual analytics), as well as a supervisor specialising in your chosen application area.

This degree is an entirely research-based master's programme. The majority of your time will be spent working on your research project in the library, laboratories or elsewhere, under the direction of your supervisors.

Alongside your independent study, you will also have tutorial lectures in the key areas of machine learning and visual analytics. You will have regular meetings with your supervisor to review progress and plan future activities. You may also spend brief periods at partner sites, for example where your project involves collaboration with industry.

Full-time students are expected to attend for an average of 35 hours per week and part-time students for an average of 12 hours per week.

Assessment

Assessment will be based on a final dissertation of approximately 30,000 words. Two independent examiners will be appointed who will read and evaluate the dissertation, following which you will be invited to make an oral defence of your work.

How can the MSc Data Science by Research support your career?

This degree is specifically designed to equip graduates with the knowledge and skills required to fulfil large corporate data requirements, and allow you to progress to the post of data scientist.

Data scientists are responsible for extracting understanding from large quantities of data, by building generalised models of trends within unstructured, heterogeneous 'big data'. Examining data from multiple sources, data scientists seek to explore patterns and discover trends, and translate these into insights and predictions which will support business decision-making, and solve business problems.

Data scientists are sought after in a broad variety of sectors, including – but by no means limited to – healthcare, broadcast, security, education, and intelligent transport.