This 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.
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.
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.
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.
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
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.
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.
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Please contact for more information on their specific requirements and equivalent qualifications from outside the UK.
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This information was correct at the time of publication.
The speed at which individuals, businesses, organisations and governments are generating data is increasing at an unprecedented rate. Feedback from employers suggest that there is a significant skills gap in Data Science – there is a need for skilled professionals to make sense of the huge amount of data being generated.
The MSc in Data Science seeks to develop graduates who will become proficient data scientists with the technical knowledge base and skills set to master the complex problems and complex data requirements emanating from Big Data. The course will enable graduates to utilise the constantly changing and evolving modern technologies that continuously reshape the way digital interaction and communication takes place.