The course starts each year in May, September and January.
The online course takes two years and three months to complete. You can take up to a maximum of five years to finish it.
We recommend a commitment of around 12–15 hours of study per week.
Units are run consecutively, with each one taking eight weeks to complete. Over the course of the year, you’ll have three short breaks, in December, April and August.
- Autumn term: September to December
Short break in December
- Spring term: January to April
Short break in April
- Summer term: May to August
Short break in August
Tailored to fit your lifestyle, our online course is perfectly suited to individuals with full-time work and/or family commitments. Online study offers multiple ways to interact with the curriculum, all designed to fit around your schedule.
Tuition fees are paid at £833* per 10 credit unit. Fees can be paid in instalments corresponding with the course units. To qualify for an MSc, you will need a total of 180 credits.
The course unlocks management and consultancy careers in business analytics, in a number of diverse industries and sectors. Recent graduates of the campus-based programme have been successfully employed in data analytics, consulting, auditing and software engineering roles.
As a student, you’ll have access to the latest IBM tools and business intelligence software including Tableau, SAS, Qlik and Cognos, helping you design reports and extract meaningful data insights.
Students are assessed through a variety of methods, including essays, reports, group work, case studies and presentations. In some cases, relevant work experience can be included as curriculum credit. Explore the course curriculum.
To enrol on our MSc course, the following criteria apply:
- Typically, you should have a first or second class bachelor’s honours degree or international equivalent
- To apply for this course, you must have an undergraduate degree in a subject with high quantitative content such as mathematics/statistics, computer science, engineering, physics, chemistry, biology, economics, or a quantitative social science. We may consider other subjects if they have enough quantitative content
- Previous experience of computer programming is an advantage. You should include information about your quantitative and programming experience in your personal statement
- If English is not your first language, but you completed your degree in the UK within the last 2 years prior to the start of the course, you may be exempt from our English language requirements.
- Alternatively, you have passed the IELTS academic test with a grade of no less than 7.0 overall, with at least 6.5 in all of the four parts (reading, listening, writing and speaking)
Our MSc courses are flexible and designed to fit around your lifestyle and work commitments. We provide an open environment to help you learn both the data science skills and the business techniques necessary for a career in business analytics. In addition to our faculty of highly acclaimed teaching staff, we have links to a wide range of companies worldwide, with units run in partnership with IBM and SAS.
The course is 100% online, so there is no need to visit campus at any point during your studies. You can attend campus, if you wish, for instance if you want to use the library, but it’s not a requirement.
Our online environment features regular interaction with your lecturers and fellow students to give it that classroom feel. To help you connect with the content, the environment includes video content, case studies and a library of digital resources. Although our online courses allow you to study at a time and place that suits you, we do ask that you complete your assignments against a set schedule.
The main programming language we use in more than 50% of the course units is R. Some units like Machine Learning and Data Mining have elements of Python, but its not a core curriculum programming language. The remainder uses a combination of other languages (e.g. Visual Basic, SQL), as well as various business intelligence software tools (e.g. Tableau Desktop, IBM Cognos).
The dissertation encompasses almost everything you will learn throughout the course. You can focus your dissertation on a particular topic or a problem you have within your organisation, or you can choose a topic suggested by one of our supervisors here at Bath.
The degree certificate and transcript do not mention ‘online’, and the degree qualification is equivalent to a full-time campus degree.
All your required reading will be made available via the University Library’s reading list system and will be available to read electronically. Once you have fully registered as a student and received your university username and password, you will be able to access the Library's services and online resources.
It is recommended you have the following operating systems in order to fully enjoy the benefits of our Virtual Learning Environment (VLE): either Windows 8 or Windows 10, Mac OS X 10.8 or later. You will need RAM of 4 GB or more and high-speed/broadband connection. If you are unsure, you can test your internet speed here.
It is also important you have an up-to-date web browser, listed here: Chrome (version 30.0 or higher), Firefox (version 25.0 or higher), Microsoft Edge or Internet Explorer (version 10 or higher), or Safari (version 6 or higher). Cookies must be enabled in your browser for our VLE to function correctly. Most browsers have cookies enabled by default. If you are unsure whether your browser is configured properly, please contact technical support for advice.
Within the VLE you can access a discussion forum within each unit where you can post questions to your tutor and fellow students. You can also email any questions you may have to your lecturer. If you are seeking support, you can contact our student support team, or if you are having a technical issue, you can ask our 24/7 technical support team.
You are welcome to visit the university if you wish, where you can access facilities such as our physical library, as well as our online library. You can apply for a library card by emailing our library services with your name, address and photo.
One of the main forms of interaction is on discussion forums on our VLE, where some of the weekly activities involve you sharing your thoughts in discussions with your fellow classmates. You also have the option to message each other and connect via email, text, WhatsApp and other social media platforms.
Added value could range from a better approach to data organisation, structuring and database design, faster out-of-box approaches to provide rapid exploratory insights from data, and finally the know-how and techniques to implement optimisation methods, forecasting and machine learning for data mining.
All business intelligence tools used in class will be provided as either a download and local install or used in a cloud-based environment with a clear pathway to access. We will work with multiple tools - Tableau, SAS and a selection from the IBM portfolio (Cognos, SPSS, Watson), Visual Basic, and more (but in smaller time footprint). We keep this list updated as the landscape of BI software evolves.
We will also use R programming statistical language to unlock advanced functionality for data processing, scraping, exploration, visualisation, modelling/forecasting and interactive reporting.
Cognos, SPSS + SPSS Modeller and Watson are all used, possibly more depending on how the IBM software portfolio evolves in the next year.
The key tool for predictive analytics will be R (combined with minor use of Python), where we will build and dissect predictive models from scratch to understand how those models reach predictions, how we can improve them, and how to interpret/communicate results of those predictive models.
All software and licences with installation instructions will be provided as part of the course – in some cases this will require online registration for student licence, but you will be provided with all required information. Software landscape is changing rapidly, and we regularly evaluate which tools are the most appropriate to use in order to maximise value for our students in terms of practical application. At the moment, and depending on the unit, students use selection of the following tools:
- R (core programming language, you will be taught how to use and apply it in a range of units)
- R Studio (IDE for R)
- Python (in minor capacity, mainly as part of Data Mining and Machine Learning)
- LaTeX (optional, for reporting)
- Tableau Desktop
- SAS Studio
- IBM software portfolio (e.g. SPSS Modeller, Cognos)
- MS Excel and MS Access
You are also asked to write SQL statements in MS Access to retrieve certain data from databases.