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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:
- learn from the experts – this course has been co-designed and co-delivered by ARU, one of the top providers of apprenticeships in the country and Cambridge Spark, who are leaders in personalised AI driven training in data science and software engineering
- hackathon-style bootcamp simulating the role of a data scientist in a real-world environment
- no tuition fees: degree apprenticeships are funded by your employers and the Government
- a vibrant learning community that gives you opportunities to network with like-minded professionals
- online modules will be delivered via CANVAS, ARU’s digital learning platform; and from K.A.T.E.® (Knowledge Assessment Teaching Engine ) which is Cambridge Spark’s innovative AI-powered learning platform, which provides instant feedback on code within an industry-simulated environment.
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
Exploratory Data AnalysisThis 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 TechniquesMachine 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 DataData 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.
Deep Learning and ApplicationsDeep 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 AnalysisThe 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 BootcampThe 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 ProjectThis 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 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.