This course is open for applications
About this course
- Entry year:
- Course code:
- Computer Science and Creative Technologies
- One year full-time; two years part-time
- Full-time; part-time
- Programme leader:
- Dr Paul Matthews
- Key fact:
- Work with us full or part time on our industry-focused data science master's, developing the skills you need to design and implement data science projects.
Page last updated 3 February 2020
Why study data science?
Bringing together skills in data management, analytics and artificial intelligence, data scientists work with organisations to draw competitive or efficiency-related insights from their data.
It's a field that's expected to soon make up at least a quarter of all digital jobs, and has been highlighted as a major skills gap in the government's recent industrial strategy.
Why UWE Bristol?
If you're looking to specialise or upskill in this field, this master's will equip you to apply data science techniques in your current role and organisation, or to progress onto new work opportunities.
Looking at the full data science pipeline, you'll learn to understand organisational requirements and ethical conduct; design research studies; and employ data engineering skills to gather, transform and clean small and large-scale data.
Gain the skills and tools to design and implement data science projects and programmes to solve real business and societal issues.
Undertake exploratory analysis, using statistics, machine learning and predictive modelling.
Present and communicate results to stakeholders, and become adept at implementing production workflows and solutions.
You'll work on live data science projects, using data and issues from your own company (if applicable) and those of our industrial partners.
Where can it take me?
The course has been developed as part of the Institute of Coding's University Learners employability initiative, to ensure it equips data scientists with the skills needed by industry.
Your newly-acquired skills will set you up well for work as a data scientist, business analyst, data engineer or chief data officer.
The optional modules listed are those that are most likely to be available, but they may be subject to change.
You will study:
- Data Science Team Project
- Introduction to Programming and Data Science
- Data Management Fundamentals
Plus, optional modules from:
- Designing the User Experience
- Linked Open Data and IOT
- Social Media and Web Science
- Machine Learning
- Big Data
- Business Intelligence and Data Visualisation
- Advanced Statistics.
This structure is for full-time students only. Part-time students study the same modules but the delivery pattern will be different.
The University continually enhances our offer by responding to feedback from our students and other stakeholders, ensuring the curriculum is kept up to date and our graduates are equipped with the knowledge and skills they need for the real world. This may result in changes to the course. If changes to your course are approved, we will inform you.
Learning and Teaching
The course is taught through a mix of context, theory and hands-on practice, with both individual and group learning activities built in.
Learn to use statistical inference, modelling and analysis, machine learning and predictive analytics.
Understand how to store, process and analyse big data.
Build skills in evidence-based communication, argumentation and data visualisation.
Implement data science projects from end to end, using real data to address business, health and sustainability problems.
Gain exposure to a range of current data science methods and tools.
Take part in a substantial interdisciplinary group project and a research dissertation on a particular aspect of data science.
You'll have access to extracurricular opportunities such as team competitions, data hackathons and paid projects for external clients through our enterprise studio network, The Foundry.
Mentoring will be available for self-organised student teams taking part in data science competitions and hackathons.
See our full glossary of learning and teaching terms.
Full-time (over one year): 8 hours a week of teaching and related activities, and 16 hours a week on self-directed study.
Part-time (over two years): 4 hours a week of teaching and related activities, and 8 hours a week on self-directed study.
Assessment will be through practical coursework, vivas, presentations and portfolios. The number of exams you take will depend on your optional module choices.
See our full glossary of assessment terms.
You'll find everything you need for your studies on our Frenchay Campus, including PC labs for module and self-study, and access to virtual machines and cloud-based environments to build your experience of big data solutions.
You'll have 24-hour access to the UWE Bristol library, as well as access to leading resources, specialist journals and publications through our online portal.
Graduate students have a dedicated space on the main campus, with teaching rooms and informal areas. Each course has a student adviser who provides pastoral support and general advice.
Learn more about UWE Bristol's facilities and resources.
Careers / Further study
Your knowledge of the latest data science methods and tools will put you in a strong position to secure work as a data scientist, business analyst, data engineer or develop a career path toward chief data officer.
Full time course
|Home/EU-Full Time-Award Fee||8000|
|Home/EU-Full Time-Module Fee (15 Credit)||667|
|International-Full Time-Award Fee||13750|
|International-Full Time-Module Fee (15 Credit)||1146|
|Offshore-Full Time-Award Fee||13750|
|Offshore-Full Time-Module Fee (15 Credit)||1146|
Part time course
|Home/EU-Part Time-Module Fee (15 Credit)||667|
|Offshore-Part Time-Module Fee (15 Credit)||1146|
Supplementary fee information
See our funding pages for more information.
We normally require an honours degree at 2:2 or equivalent in a relevant subject. Experience with quantitative methods and/or coding is highly recommended.
Relevant subjects include: Computer Science, IT or other computing subjects, Maths, Statistics, any Engineering subject, any quantitative subject such as Physics, Chemistry, Business, Marketing, Economics, Psychology and Social Sciences.
We can consider applicants who do not meet the normal entry requirements, but who have relevant professional experience or qualifications. In your application, you should describe in detail your professional experience and qualifications.
How to apply
For further information
- Email: Admissions@uwe.ac.uk
- Telephone: +44 (0)117 32 83333