Data Science - BSc (Hons)

More about this course

Designed by academics from both Mathematics and Applied Computing backgrounds, this course is made up of fine-tuned modules which are prepared with your future in mind. The course will foster your learning development using a range of tools and big data platforms, allowing you to continue to specialise in data engineering, analytics, big data visualisation, statistical modelling and machine learning.

During your studies you’ll be encouraged to:

• apply maths, statistics and science practice
• recognise and exploit business opportunities using data science innovation
• find a solution to domain-specific problems using data science capability
• utilise a range of coding practices
• build scalable data products for strategic or operational business and contribute through the product life cycle
• use tools such as Spark, Kafka, Hadoop, Oracle, SQL Server, Linux, Apache Airflow, RStudio, Python - Jupyter, Tableau, and D3 technology

Your learning will see you attend a variety of scheduled sessions such as lectures, tutorials, and workshops. This will be further developed by your revision of module materials and learning exercises outside of scheduled teaching hours. Throughout your learning experience you’ll find the teaching team on hand to support you.

What’s more, we have a wealth of appropriate blended learning technologies, such as the University’s virtual learning environment WebLearn, our library’s e-books and our online databases. These will further facilitate and support your learning, in particular to:

• deliver content
• encourage your active learning
• provide formative and summative assessments with prompt feedback
• enhance your course engagement

The specialist nature of this course will allow you to explore and experience advanced techniques in data science and data analytics. You’ll acquire practical skills, often first-hand from an external organisation, which will prepare you for your future as a data scientist.

Assessment

You’ll be provided with opportunities to develop an understanding of good academic practice, as well as the skills necessary to demonstrate this. In particular, you’ll be encouraged to complete weekly tutorial and workshop exercises as well as periodic formative diagnostic tests to enhance your learning. During tutorial and workshop sessions you’ll receive ongoing support and feedback on your work to promote engagement and provide the basis for tackling the summative assessments.

You’ll be assessed by a variety of methods throughout your studies. Module assessment typically consists of a combination of assessment methods including:

• coursework
• in-class tests
• exams

Coursework can include an artifact such as an output of dataset analysis, application of algorithms, data trends or program code in addition to a written report/essay. The volume, timing and nature of assessment will enable you to demonstrate the extent to which you have achieved the intended learning outcomes.

Formative and summative feedback will be provided using a variety of methods and approaches, such as learning technologies and one to one and group presentations of the submitted work at various points throughout the teaching period.

Entry requirements

In addition to the University's standard entry requirements, you should have:

• a minimum grade C in three A levels (or a minimum of 96 UCAS points from an equivalent Level 3 qualification, eg BTEC Level 3 Extended Diploma, Advanced Diploma, Progression Diploma or Access to Higher Education Diploma of 60 Credits)
• English language and Mathematics GCSEs at grade C/4 or above (or equivalent)

Applicants with relevant professional qualifications or extensive professional experience will also be considered.

If you don’t have traditional qualifications or can’t meet the entry requirements for this undergraduate degree, you may still be able to gain entry to the four-year Data Science (including foundation year) BSc programme.

All applicants must be able to demonstrate proficiency in the English language. Applicants who require a Tier 4 student visa may need to provide a Secure English Language Test (SELT) such as Academic IELTS. For more information about English qualifications please see our English language requirements.

If you need (or wish) to improve your English before starting your degree, the University offers a Pre-sessional Academic English course to help you build your confidence and reach the level of English you require.

Modular structure

Where this course can take you

This course will prepare you to work in the field of data analytics, data programming, data visualisation, IT data consultation, big data solution designing or data solution development.

This degree award can put you in a position to apply to companies such as Facebook, Mastercard, Amazon, Microsoft or the BBC for roles such as Junior Data Scientist, Data Science Operational Officer or Associate Data Analyst. 

This course is also excellent preparation for further study or research.

Discover Uni – key statistics about this course

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When to apply

The University and Colleges Admissions Service (UCAS) accepts applications for full-time courses starting in September from one year before the start of the course. Our UCAS institution code is L68.

If you will be applying direct to the University you are advised to apply as early as possible as we will only be able to consider your application if there are places available on the course.