Course details
This degree has three stages, each comprising 120 credits.
- Stage 1 has one 30-credit mathematics, one 30-credit statistics and two 30-credit computing & IT modules.
- Next, in Stage 2, you’ll study one 30-credit applied mathematics module, two 30-credit statistics modules and one 30-credit computing & IT module.
- Finally, in Stage 3, you’ll study one 30-credit statistics module and one 30-credit computing & IT module, and choose two more 30-credit modules from a selection of computing & IT, statistics and applied mathematics modules.
Optional Access module – visit Entry requirements to find out about starting this course with a preparatory Access module.
You’ll start by studying a range of fundamental topics in mathematics, statistics and computing.
You’ll deepen your understanding of statistics, learn about a variety of mathematical methods, and become a computational thinker.
You’ll deepen your understanding of building both statistical and computational models, and choose from a range of other subject areas.
We regularly review our curriculum; therefore, the qualification described on this page – including its availability, its structure, and available modules – may change over time. If we make changes to this qualification, we’ll update this page as soon as possible. Once you’ve registered or are studying this qualification, where practicable, we’ll inform you in good time of any upcoming changes. If you’d like to know more about the circumstances in which the University might make changes to the curriculum, see our Academic Regulations or contact us. This description was last updated on 20 March 2019.
Accessibility
We make all our qualifications as accessible as possible and have a comprehensive range of services to support all our students. The BSc (Honours) Data Science uses a variety of study materials and has the following elements:
- studying a mixture of printed and online material – online learning resources may include websites, audio/video media clips, and interactive activities such as online quizzes
- using mathematical and scientific expressions, notations and associated techniques
- using and/or producing diagrams and/or screenshots
- undertaking practical work
- using specialist software, including tools for programming languages
- finding external/third-party material online.
For more detailed information, see the Accessibility Statements on individual module descriptions. If you feel you may need additional support, visit Disability support to find more about what we offer.
Learning outcomes, teaching and assessment
This qualification develops your learning in four main areas:
- Knowledge and understanding
- Cognitive skills
- Practical and professional skills
- Key skills
The level and depth of your learning gradually increases as you work through the qualification. You’ll be supported throughout by the OU’s unique style of teaching and assessment – which includes a personal tutor to guide and comment on your work; top quality course texts; e-learning resources like podcasts, interactive media and online materials; tutorial groups and community forums.
Read the detailed learning outcomes here
Credit transfer
If you’ve already completed some study at another university, you may be able to count it towards your Open University qualification – reducing the number of modules you need to study.
You should apply for credit transfer before you register, at least 4 weeks before the registration closing date. Just tell us what you studied, where and when, and we’ll compare this against the learning outcomes for your chosen course.
For more details and an application form, visit our Credit Transfer website.
Classification of your degree
On successfully completing this undergraduate course, you'll be awarded the BSc (Honours) Data Science degree. The class of degree (first, upper second, lower second or third-class honours) depends on your grades at Stages 2 and 3.
You'll have the opportunity to attend a degree ceremony.
Recognition in your country
If you intend to use your Open University qualifications to seek work or undertake further study outside the UK, we recommend checking whether your intended qualification will meet local requirements for your chosen career. Read recognition in my country.
Regulations
As a student of The Open University, you should be aware of the content of the qualification-specific regulations below and the academic regulations that are available on our Student Policies and Regulations website.
Entry requirements
There are no formal entry requirements to study this qualification, but it’s very important you’re well prepared to study mathematics at this level, and are able to read and write to a good standard of English. Ideally, you’ll also have some basic knowledge of computing.
You do need experience in mathematics – you should have a working knowledge of all of the following topics:
- arithmetic of whole numbers, decimals and fractions (including negative numbers, powers and roots)
- algebraic manipulation, such as multiplying out brackets, factorising simple expressions, solving linear and quadratic equations, manipulating algebraic fractions and manipulating powers of variables
- percentages, ratio and proportion
- simple inequalities
- exponential functions, logarithms and the rules for manipulating them.
Check you’re ready for Essential mathematics 1 (MST124) with our self-assessed quiz.
Having tried the quiz, if you don’t feel ready for MST124, you can study an additional module to give you the mathematical knowledge and skills you need. Contact us to find out more.
How much time do I need?
- Most of our students study part time, completing 60 credits a year.
- This will usually mean studying for 16–18 hours a week.
1The compulsory Stage 3 module, Applied statistical modelling (M348), starts for the first time in October 2022. If you start this qualification in October 2019, you will be able to study Stages 1 and 2 full time; however, you'll be able to study Stage 3 only part time. You can study Stage 3 full time from October 2022.
Skills for career development
The ability to analyse complex data sets is a much sought after skill in the modern workplace. The degree will equip you with knowledge of data analysis and modelling from statistics, applied mathematics and computer science. In addition, you’ll develop important transferable skills such as communication, time management and problem solving.
Career relevance
Data scientists are highly sought after in virtually all work places. The use of data science in social media, online commerce and government has revolutionised the digital economy, with employers across both the public and private sectors now recruiting data scientists to identify and solve complex business problems. Data scientists are at the heart of supporting strategic and operational decision making. They are needed in all areas of employment including business intelligence, management, biology, economics, education, engineering, environment studies, finance, government, logistics, medicine, meteorology, market research, sport and multinational businesses.
Exploring your options
Once you register with us (and for up to three years after you finish your studies), you’ll have full access to our careers service for a wide range of information and advice – including online forums, website, interview simulation, vacancy service as well as the option to email or speak to a careers adviser. Some areas of the careers service website are available for you to see now, including help with looking for and applying for jobs. You can also read more general information about how OU study enhances your career.
In the meantime, if you want to do some research around this qualification and where it might take you, we’ve put together a list of relevant job titles as a starting point (note that some careers may require further study, training and/or work experience beyond your degree):
- applied mathematician
- business analyst
- business manager
- chief data officer
- computer scientist
- data analyst
- data architect
- data engineer
- data scientist
- information manager
- machine learning application developer
- marketing and commerce practitioner
- statistician