Postgraduate (2 years part-time, work-based)
This course is delivered through blended learning: work-based study, online through a combination of our Learning Management System, Canvas and Cambridge Spark’s K.A.T.E.® platform, plus immersive teaching weeks and a hackathon-style bootcamp on our Cambridge campus.
Develop and apply specialist data science tools and techniques to process large, complex datasets in order to extract and derive valuable insights to inform decision making within your organisation. Gain an MSc Digital and Technology Solutions (Data Analytics) degree while you work, with fees funded by your employer and the Government.
Data science, in particular coupled with artificial intelligence, promises to provide the tools for enhanced technologies, business models and decision making across a large number of fields, from industrial automation, manufacturing, transport, banking and cyber security to health and social care.
Through working on real-world datasets and industry-simulated projects, you will learn the skills and knowledge required to apply the latest advanced data science tools and techniques such as data engineering and deep learning.
The MSc Digital and Technology Solutions (Data Analytics) will be delivered in partnership with Cambridge Spark, a leading provider of continuous professional development training for developers and data scientists.
This cutting-edge course is taught as a blend of immersive teaching weeks, online study and a hackathon-style bootcamp which simulate real-world environments.
20% of your learning will be classed as ‘off the job’, which means that you'll be learning during normal working hours either in your place of work or outside – but not part of your normal working duties.
As well as learning while you earn, the benefits of studying this course include:
Data Scientists are uniquely capable of applying powerful data analytics to unlock valuable insights. Data Science is one of the most in-demand skillsets and organisations are using the Apprenticeship Levy to cost effectively develop their workforce.
There is a recognised significant skills gap in data science based systems specialists in the industry, nationally and internationally. This is despite data scientist roles growing over 650% since 2012, with machine learning engineers, data scientists, and big data engineers ranking among the top emerging jobs*.
Employers have highlighted the importance of data science and its potential to revolutionise a number of industries, from social sciences, physics and engineering to market analysis and banking, while creating significant employment opportunities for data analysts, machine learning specialists and data specialists.
Typical job roles include big data analyst, data and insight analyst, data science specialist, data management specialist and analytics lead.
*U.S. Bureau of Labor Statistics.
Assessment will be via a variety of methods including time constrained assessments, coursework assignments and project.
The dissertation project and module case studies assess your ability to analyse situations, identify key issues, select, synthesise and apply techniques and skills from different modules and to be able to evaluate the appropriateness of their solutions when compared to industrial practice.
The dissertation artefact will be based on a real-world scenario related to or actually part of an actual piece of project work in a company.
End Point Assessment (EPA)
This apprenticeship features an ‘integrated’ End Point Assessment. It gives you the opportunity to demonstrate that you have attained the skills, knowledge and behaviours set out on the standard.
There are two parts to the end-point assessment:
You will need:
Applicants who do not meet the above requirements may be also considered on a case-by-case basis and may require an interview.
ARU’s standard procedures for admission with credit will apply if you wish to be considered for Accredited Prior Learning (APL) and Accredited Prior Experiential Learning (APEL) for entry into Year 2 or later of the course.
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