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Data Science
Data Science MSc
Data Science MSc

Data Science MSc

  • ID:US440013
  • Level:Master's Degree
  • Duration:
  • Intake:
  • Type:Full-time

Fees (GBP)

  • TuitionFee/Year:£14,500.00
  • LivingFee/Year:£12,000.00
  • InsuranceFee/Year:£300.00
  • TuitionFee/Year:£14,500.00
  • LivingFee/Year:£12,000.00
  • InsuranceFee/Year:£300.00
Estimated Total/program:
£26,800.00
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Admission Requirements

Entry Requirements

English Requirements

  • ​IELTS with an overall score of 6.0 and at least 5.5 in each component 
  • PTE 56 with no less than 51 in each skill.

Course Information

The UK Government has identified a huge shortage of specialists in data science and artificial intelligence. The University of Sunderland is looking for a new generation of idealists, visionaries and problem solvers to help business, industry and government make the right decisions in our increasingly complex and uncertain world.

This course is designed for students who have computing, STEM experience or prior experience in working with data (e.g. statistics), to gain the knowledge and skills required to work in data science or data analytics in the real world. We want you to bring your background with you, whether you graduated in Psychology, Business, Geography or Law. We are looking for people who ask questions, who don’t jump to conclusions, and who respond with caution. We want people who are able to find and communicate the best possible solutions.

On this course, you'll study subjects including the fundamentals of data science, data mining, machine learning, data analytics and visualisation, and security of big data. This highly practical course means you’ll have the opportunity to experience the latest technologies and tools used in industry, giving you the confidence to be productive and effective when you go out into the workplace.

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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. 

 

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Career Opportunity

Career Opportunity

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”.

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.

Ability to settle

Overseas Student Health Cover

Insurance-Single: 300 GBP/year

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