Data Science MSc

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Course starts: 16 September 2019Apply now

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Harness new forms of data with increasingly powerful computer techniques. Gain the technical and practical skills to analyse big data. Study a unique course developed according to the needs of the employment sector.

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Overview

This newly developed Data Science course will provide you with the technical and practical skills to analyse big data that is key to success in future business, digital media and science. Study industry-specific topics and specialise in areas such as data mining, machine learning, data analytics and visualisation, and security of big data.

Our close links to industry and businesses in the North East, as well as the research expertise of our academics, makes this course unique and ensures that the course structure is developed according to the needs of the employment sector.

Upcoming start dates
28 January 2019
16 September 2019

Why us?

  • The digital sector will require over 2 million new recruits by 2020, according to Development Economics 2015
  • The department has been a Cisco partner for over 10 years

Course structure

We use a wide variety of teaching and learning methods which include lectures, group work, research, discussion groups, seminars, tutorials and practical laboratory sessions. Compared to an undergraduate course, you will find that this Masters requires a higher level of independent working.

Assessment methods include written reports and research papers, practical assignments and the Masters project.

This course is also delivered at our Hong Kong campus.

Modules on this course include:

  • Data Science Fundamentals (30 credits)

Learn how to use different types of data and understand how to fuse more than one dataset together. Apply a full range of traditional and intelligent analytics to a variety of datasets and make use of modern data science / big data platforms and languages. Cover techniques and tools for presenting and visualisation.

  • Data Science Product Development (30 credits)

Learn how to design and develop a data science product to solve a challenging real world problem, based on a systemic literature review on state of the art data science software technologies and project development methodologies, prototyping the product with end users’ evaluations. Produce a project summary report.

  • Machine Learning and Data Analytics (30 credits)

Study three interrelated subjects: machine learning, data mining, and data analytics including relevant professional, ethical, social and legal aspects. Focus on information and knowledge management, problems with data, approaches to selection of data analytics tools, principles of modelling and simulation, and operations research. Examine the trends, tools, and current developments in the area of machine learning, data mining and data analytics and their practical applications

  • Technology Management for Organisations (30 credits)

Learn to apply the principles, policies and procedures of cybersecurity and data science to provide resilient and robust organisational solutions for secure and valuable information. Develop techniques and use tools that will enable you to undertake critical analysis of the challenges and opportunities of using cybersecurity to mitigate and manage risk to data and enable business continuity in the case of data breaches. Develop a critical understanding of governance, standards, audit, assurance and review in order to evaluate the challenges in managing technology. 

  • Computing Masters Project (60 credits)

Develop a practical deliverable and investigate an area of academic research through the support of a sponsor for example: an IT strategy; an investigative study; a technically challenging artefact (e.g. a feasibility study, design, implementation, re-engineered solution); or undertake a theoretical review based on a novel research question (provided by a research active member of staff). Underpin the project with a literature review that is a conceptual framework of your study - a systematic synthesis of concepts, assumptions, expectations, beliefs, and theories that supports and informs your research. 

 

Some modules have prerequisites. Read more about what this means in our Help and Advice article.

  • Our outstanding IT facilities include the David Goldman Informatics Centre, which has hundreds of computers so it’s easy to find a free workstation with the software you need.

    We are an accredited Cisco Academy and have two laboratories packed with Cisco networking equipment including routers, switches, terminals and specialist equipment for simulating frame relay and ISDN links.

    We host high-performance computing platforms, including a Big Data machine and a High Performance Computing Cluster system, for concurrent processing of complex computational tasks. We also have the equipment and licences for our own public mobile cellular network.

     

     

     

     

     

    IT facilities for computing, networks and big data
  • Some of the most important sources for computing students include:

    • British Standards Online, which offers more than 35,000 documents covering specifications for products, dimensions, performance and codes of practice
    • Association of Computing Machinery digital library, which includes full-text articles from journals as well as conference proceedings
    • Science Direct, which offers more than 18,000 full-text journals published by Elsevier
    • Archives of publications from Emerald, including over 35,000 full-text articles dating back to 1994 on a range of subjects including technology
    • Business Source Elite from EBSCO Publishing, which covers hundreds of journals and includes articles on topics such as e-commerce and information management
    Library Services for computing
  • Map and directions

Facilities

Study in a vibrant and supporting environment with state-of-the-art facilities and excellent learning resources. We are also an accredited cisco academy and have two laboratories packed with cisco networking equipment.

 

 

 

 

 

 

 

Entry requirements

We require applicants to hold an undergraduate degree with a classification of 2:2 or above in a computing or related discipline (mathematics, statistics, engineering), or 2:1 or above in a relevant non-computing or related discipline (which has numeracy included and/or application of big data as a significant theme).

If English is not your first language, please see our English language requirements.

Fees and finance

2018/19 fees

The fee for this course is:

  • £6,800 if you are from the UK or EU and studying full-time
  • £13,200 if you are from outside the EU and studying full-time
  • £344 per 10 credits if you are studying part-time (UK/EU applicants only)

2019/20 fees

The annual fee for this course will be:

  • £7,000 if you are from the UK or EU and studying full-time
  • £13,500 if you are from outside the EU and studying full-time
  • £361 per 10 credits if you are studying part-time (UK/EU applicants only)

Part-time courses are not available to international students. If you are not sure whether you qualify as a UK, EU or international student, find out more in our Help and Advice article.

Take a look at the Your Finances section to find out about the scholarships and bursaries that may be available to you.

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This information was correct at the time of publication.

The Sir Tom Cowie Campus at St Peter's by night

Employment

The digital sector will require 300,000 new recruits by 2020, with a specific specialist demand of ‘high-level IT specialists, such as IT architects, big data and security specialists’ (UK commission for Employment and Skills 2013). According to the McKinsey Report (2011), “the demand of people with data science skills is predicted to double over the next five years”.

Employability

Job trends data shows a 15,000% increase in the job prospects between 2011 and 2012, recognising big data as the ‘next big thing’ to revolutionise how we work, live and communicate (Indeed, 2016).

Progress in some of the most attractive fields and industries, including government agencies, high technology companies, consulting and market research firms. Benefit from the University’s close links with businesses and employers in the North East and join an industry-driven programme.

Businesses and industries across the UK have identified a skills gap in data science and currently the role of a Data Scientist is one of the highest paid jobs in the computing discipline.

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