Digital and Technology Solutions Specialist Degree Apprenticeship MSc

Postgraduate (2 years part-time, work-based)

Blended learning, Cambridge

September

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.

Overview

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.

Full description
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.
Raoul-Gabriel Urma
Founder, Cambridge Spark

Careers

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.

Modules & assessment

Year one, core modules

  • Exploratory Data Analysis
    This module provides a sound basis in data analysis. The module introduces feature engineering and selection, including variance thresholding, correlation and checking for multicollinearity. You will also be introduced to the principal component analysis (PCA) including making sense of high dimensional data, dimensionality reduction, intuition linear algebra background and algorithm, using Pandas and Scikit-learn.
  • Machine Learning Techniques
    Machine learning is a sub-discipline of artificial intelligence that deals with teaching the computer to act without being programmed. In this module you will learn about the tools and algorithms that can be used to create machine learning models. Big data and their economic, legal and ethical aspects are examined, along with data acquisition and pre-processing methods that are used to make these suitable for machine learning algorithms. You will also look into how large data sets should be divided into a training set and a test set.
  • Data Engineering and Big Data
    Data engineering is a process to design, build and manage the information or 'big data' infrastructure. It gives an understanding of how to develop the architecture that helps analyse and process data in the way the organisation needs it. This module will examine the entire data life cycle, including data creation, modelling, representation, analysis, maintenance and disposal. As the majority of data is stored in databases, this module will provide an introduction to various types of databases and discuss the methods to ensure clean, reliable, and performative access to data.

Year two, core modules

  • Deep Learning and Applications
    Deep learning and its applications have revolutionised numerous fields in recent years. This module explores the two main areas of neural networks and deep learning. You will start analysing the structure of neural networks, from the theoretical aspects to the practical implementations. You will then move to training a neural network using Keras. Then, this module will explore the convolutional neural networks (CNNs) and introduce deep learning from the convolutional operator and stacking convolutional layers to regularisation, batch normalisation and data augmentation.
  • Advanced Time Series Analysis
    The module will provide an introduction to emerging techniques allowing data scientists and practitioners to study and investigate nonlinear time series. It will offer a collection of tools designed to dive deep down into underlying structures of data, allowing future data scientists to detect whether stochastic or deterministic dynamics most likely drive observed complexity. In other words, this module will teach you how to become a 'data detective' accumulating hard empirical evidence supporting your modelling approach.
  • Machine Learning Bootcamp
    The module builds up on previous knowledge gained in the course. It aims to test, through real life scenarios, as part of practical projects, concepts of artificial intelligence and machine learning techniques that enable a system to learn from data rather than through explicit programming. These techniques are becoming essential in business operation innovation and more generally in generating more efficient workflows.
  • Major Project
    This module supports you in the preparation and submission of a Masters stage dissertation. The topic may be drawn from a variety of sources including: school research groups, previous/current work experience, the company in which you are currently employed, a lecturer suggested topic or a professional subject of your specific interest (if suitable supervision is available). The chosen topic will require you to identify/formulate problems and issues, conduct literature reviews, evaluate information, investigate and adopt suitable development methodologies, determine solutions, develop hardware, software and/or media artefacts as appropriate, process data, critically appraise and present your findings using a variety of media.

Assessment

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:

  • project report
  • a professional discussion.

Where you'll study

Where can I study?

Blended learning
Person using laptop

Study at a time that suits you, using our learning management system.

More about blended learning

Cambridge
Lord Ashcroft Building on our Cambridge campus

Our campus is close to the centre of Cambridge, often described as the perfect student city.

Explore our Cambridge campus

Fees & funding

Course fees

Degree apprenticeships are funded by your employer and the Government

£0

Entry requirements

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Main

You will need:

  • Full-time employment in a relevant role
  • First or second class honours in a scientific discipline
  • At least A level Maths or Statistics (or equivalent)
  • Level 2/GCSE English and Maths
  • Intermediate level knowledge of Python* tested via pre qualifier quiz
  • If English is not your first language, you will be expected to demonstrate a certificated list of proficiency of at least IELTS 6.0 or above
  • You’ll also need a suitable computer with internet connection, together with sufficient IT competence to make effective use of word processing, internet and email.

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.