MENU SEARCH

Data Science MSc

Postgraduate (2 years distance learning, part-time)

September

How to apply

Overview

Advance your career in data science and machine learning with our Masters degree in Data Science. Study part-time by distance learning, and spend two weeks in Cambridge at a hackathon-style bootcamp. Develop the skills you need to analyse complex data, and support business decision-making.

Full description

Careers

Data scientist roles have grown over 650% since 2012, with machine learning engineers, data scientists, and big data engineers ranking among the top emerging jobs*. But there's a recognised skills gap both in the UK and internationally.

Employers highlight the importance of data science and its potential to revolutionise a number of industries, while creating significant employment opportunities for data analysts, machine learning specialists and data specialists.

Our Employability Service is here to help give you the best chance of landing the job you want. We'll help you 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.

*U.S. Bureau of Labor Statistics.

Modules & assessment

  • 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'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 Techniques
    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 and Big Engineering
    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 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. 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.
  • 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 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.

Assessment

We'll assess you in a number of ways including time-constrained assessments, coursework assignments and a project.

Our 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 evaluate the appropriateness of solutions when compared to industrial practice.

The dissertation artefact will be based on a real-world scenario related to, or part of, an piece of project work in a company.

Where you'll study

Your faculty

The Faculty of Science & Engineering is one of the largest of the four faculties at Anglia Ruskin University. Whether you choose to study with us full-time or part-time, on campus or at a distance, there’s an option whatever your level – from a foundation degree, BSc, MSc, PhD or professional doctorate.

Whichever course you pick, you’ll gain the theory and practical skills needed to progress with confidence. Join us and you could find yourself learning in the very latest laboratories or on field trips or work placements with well-known and respected companies. You may even have the opportunity to study abroad.

Everything we do in the faculty has a singular purpose: to provide a world-class environment to create, share and advance knowledge in science, technology and engineering fields. This is key to all of our futures.

Where can I study?

Fees & funding

How do I pay my fees?

Tuition fee loan

You can take out a tuition fee loan, which you won’t need to start repaying until after your graduate. Or alternatively, there's the option to pay your fees upfront.

Loans and fee payments

Scholarships

We offer a fantastic range of ARU scholarships, which provide extra financial support while you’re at university. Some of these cover all or part of your tuition fees.

Explore ARU scholarships

International students

You must pay your fees upfront, in full or in instalments. We will also ask you for a deposit or sponsorship letter. Details will be in your offer letter.

Paying your fees

Funding for UK & EU students

It’s important to decide how to fund your course before applying. Use our finance guide for postgraduate students to learn more about postgraduate loans and other funding options.

We offer a fantastic range of ARU scholarships, which provide extra financial support while you're at university. Find out more about eligibility and how to apply.

Funding for international students

We offer a number of scholarships, as well as an early payment discount. Explore your options:

Entry requirements

Loading... Entry requirements are not currently available, please try again later.

Main

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. To find out more please contact us at distancelearning@anglia.ac.uk or call (0)1245 686707.

You will also need:

  • 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.

The 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.

International students

We welcome applications from international and EU students, and accept a range of international qualifications.

See entry qualifications for your country
English language requirements

If English is not your first language, you'll need to make sure you meet our English language requirements for postgraduate courses.

Improving your English language skills

If you don't meet our English language requirements, we offer a range of courses which could help you achieve the level required for entry onto a degree course.

We also provide our own English Language Proficiency Test (ELPT) in the UK and overseas. To find out if we are planning to hold an ELPT in your country, contact our country managers.

Suggested courses that may interest you

Artificial Intelligence and Big Data MSc

Full-time postgraduate (1 year)

Cambridge

September

Digital and Technology Solutions Specialist (Data Analytics) Degree Apprenticeship MSc

Part-time, work-based postgraduate (2 years)

Blended learning, Cambridge

September

Intelligent Systems and Machine Learning MSc

Full-time postgraduate (1 year)

Cambridge

September

Apply now

Register your interest

Email our Distance Learning team.

Register interest
We use cookies on the ARU website to improve your browsing experience, monitor how our site is used, and to aid us with advertising our services. By continuing to use our site, you're agreeing to us placing cookies outlined in our privacy policy on your device.