Advance your career in Data Science and Artificial Intelligence with our remote learning Masters, in collaboration with Anglia Ruskin University.
Who is the course for?
The Masters programme is suitable for individuals:
- With a first or second class (Hons) bachelors degree (exceptions may apply)
- With Maths or Statistics A-Levels, or equivalent (exceptions may apply)
- With an intermediate level of Python programming skills
- With a proven English language proficiency of at least IELTS 6.5 or above, if English isn’t your first language
- Looking to transition to the Data Science field, upskill to learn how to apply advanced Data Science techniques to their roles, or reskill to remain relevant within their field
- Part-time, remote learning
- Machine Learning Bootcamp in year two
- Assessed via time-constrained assignments, coursework assignments and projects
- Real-world projects in a work-simulated environment across each module, enabling learners to build up a Data Science portfolio to evidence skills to employers
- Employability and careers support
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, cybersecurity to health and social care.
Through working on real-world datasets and industry-simulated projects, you’ll acquire the skills and knowledge required to apply the latest in advanced Data Science tools and techniques, such as Data Engineering and Deep Learning.
Over the two year course, you’ll study primarily online via digital learning management systems and attend a hackathon-style bootcamp on Anglia Ruskin University’s Cambridge campus in year two.
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’ll 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 is a sub-discipline of the 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 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 lifecycle, 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 its applications have revolutionised numerous fields in recent years. This module explores the two main areas of neural networks and deep learning. We’ll start analysing the structure of neural networks, from the theoretical aspects to the practical implementations. We 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.
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.
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.
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 they are currently employed, a lecturer suggested topic or a professional subject of their 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.
Due to the specialist nature of this course, applicants are required to pass a proficiency quiz in Python before submitting an application for the course.
- First or second class honours degree in a scientific discipline
- At least A level Maths or Statistics (or equivalent)
- Intermediate level knowledge of Python (Tested via online 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 6.5 or above.
Applicants who do not meet the above requirements may be also considered on a case-by-case basis and may require an interview.
University’s standard procedures for admission with credit will apply where candidates wish to be considered for Accredited Prior Learning (APL) and Accredited Prior Expediential Learning (APEL) for entry into year 2 or later of the course.
How you’ll learn and be assessed
Your course will be taught primarily online. Online modules will be delivered via Canvas, ARU’s digital learning platform; and 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.
Assessment will be via a variety of methods, including time-constrained assessments, coursework assignments and projects.
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 – as well as your ability able to evaluate the appropriateness of their solutions when compared to industrial practice.
How you’ll be supported
on your journey
During the Master’s programme, you’ll gain the technical and domain knowledge to accurately predict trends, optimise performance, improve decision making and increase business competitiveness upon completing the degree.
In addition to this, you’ll have access to Anglia Ruskin University’s Employability Service – providing you with specialist career guidance and support to enable you to have the best chance at landing the job you want.
As a distance learning student, you’ll also benefit from help and advice on writing CVs, undertaking interviews and looking for jobs, helping you stand out from other applicants.
Interested in learning more and start your application?
If you have any questions regarding the course or you’re looking to start your application today, please complete the below form and our head of admissions will be in touch within one working day.
Get in touch now
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*.
*(U.S. Bureau of Labor Statistics).
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.
– At least A level Maths or Statistics (or equivalent)
– Intermediate level knowledge of Python (Tested via online 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 6.5 or above.
No problem! If you have a bachelors degree but are lacking in the technical side of the requirements, we recently introduced our Applied Data Analytics Bootcamp in London to enable you to meet the programming prerequisites of the Applied Data Science Bootcamp, covered across five intensive weekends.
Additionally, if you’re applying for the first intake in 2019, you also have the option of attending the Introduction to Python Bootcamp in Cambridge, taking place 14 – 16 August. Click here now to learn more and enrol.
Anglia Ruskin University’s Employability Service is there to help give you the best chance of landing the job you want. They’ll help you to improve your skills and bulk up your CV to improve your career prospects.
As a distance learning student, you’ll still benefit from help and advice on CV writing, interview techniques, job hunting, and general careers advice.
To find out more please visit their careers advice page.
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