Data Science - BSc (Hons)

with a Year in Industry

Course structure

Duration: 4 years full-time

The following modules are indicative of those offered on this programme. This listing is based on the current curriculum and may change year to year in response to new curriculum developments and innovation.

In stages 1 and 2 will you will study a number of core modules in statistics, mathematics, computer science and artificial intelligence. You spend your year between stages 2 and 3 on and industrial placement and return in stage 3 you will have a choice from a range of modules in addition to core modules.

• Stage 1
• Stage 2
• Year in industry
• Stage 3

Teaching and assessment

Teaching

Teaching is based on lectures, with practical classes and seminars, but we are also introducing more innovative ways of teaching, such as virtual learning environments and work-based tuition. 

Academic support

We provide excellent support for you throughout your time at Kent. This includes access to web-based information systems, podcasts and web forums for students who can benefit from extra help. We use innovative teaching methodologies, including BlueJ and LEGO© Mindstorms for teaching Java programming.

Teaching staff

Our staff have written internationally acclaimed textbooks for learning programming, which have been translated into eight languages and are used worldwide. 

Contact hours

For a student studying full time, each academic year of the programme will comprise 1200 learning hours which include both direct contact hours and private study hours.  The precise breakdown of hours will be subject dependent and will vary according to modules.  Please refer to the individual module details under Course Structure.

Methods of assessment will vary according to subject specialism and individual modules.  Please refer to the individual module details under Course Structure.

Programme aims

The programme aims to:

• attract and meet the needs of those contemplating a career as a data scientist
• equip students with the technical appreciation, skills and knowledge appropriate to graduates in Data Science
• develop students’ facilities of rigorous reasoning and precise expression
• develop students’ capabilities to formulate and solve problems, relevant to Data Science
• develop in students an appreciation of recent developments in Data Science, and of the links between the theory and practical application
• develop in students a logical approach to solving problems
• develop in students an enhanced capacity for independent thought and work
• ensure students are skilled in the use of relevant Data Science software
• provide students with opportunities to study advanced topics in Data Science
• engage in research at some level, and develop communication and personal skills.

Learning outcomes

Knowledge and understanding

You will gain a knowledge and understanding of:

• core mathematical principles of calculus, algebra, mathematical methods and linear algebra
• the subjects of probability and inference
• information technology skills as relevant to Data Science
• methods and techniques appropriate to Computing and Statistics
• the role of logical mathematical argument and deductive reasoning
• practice, including problem identification, deploying established approaches accurately to analyse and solve problems and testing and evaluation
• software, including programming languages and practice, tools and packages, computer applications, structuring of data and information
• the legal background, security and ethical issues involved in data science.

Intellectual skills

You will gain the ability:

• to demonstrate a reasonable understanding of the basic body of knowledge for Computing, Mathematics and Statistics used in data science
• to demonstrate a reasonable level of skill in calculation and manipulation of mathematical and statistical material written within the programme and some capability to solve problems formulated within it
• to apply a range of core concepts and principles in well-defined contexts relevant to Computing, Mathematics and Statistics used in data science
• to use logical argument
• to demonstrate skill in solving problems in Data Science by various appropriate methods
• in relevant computer skills and usage
• to work with relatively little guidance
• to present succinctly to a range of audiences rational and reasoned arguments.

Subject-specific skills

You will gain these subject-specific skills:

• ability to demonstrate knowledge of key mathematical and statistical concepts and topics, both explicitly and by applying them to the solution of problems.
• ability to demonstrate skills in codification and storage of data and in pre-processing raw data for later retrieval and analysis.
• ability to demonstrate understanding of fundamental computational concepts and algorithmic thinking, including recursive, distributed and parallel possibilities; the role of these in devising artificial intelligence/machine learning algorithms and in statistical modelling as well as in delivering innovative solutions to applied problems.
• ability to comprehend problems, abstract the essentials of problems and formulate them mathematically and in symbolic form in order to facilitate their analysis and solution.
• ability to use key aspects of statistics, artificial intelligence/machine learning and optimisation in a principled fashion to address the challenges of small and large data sets in well-defined contexts, showing judgement in the selection and application of tools and techniques.
• ability to use computational and more general IT facilities as an aid to mathematical and statistical processes
• ability to present mathematical and statistical arguments and the conclusions from them with clarity and accuracy
• ability to critically evaluate and analyse complex problems, argument and evidence, including those with incomplete information, and devise appropriate computing solutions, within the constraints of a budget.

Transferable skills

You gain the following transferable skills:

• problem-solving skills, relating to qualitative and quantitative information
• communication skills
• numeracy and computational skills
• information technology skills such as word-processing, internet communication, etc.
• personal and interpersonal skills and management skills
• time-management and organisational skills, as evidenced by the ability to plan and implement efficient and effective modes of working
• study skills needed for continuing professional development.

Careers

Graduate destinations

Our graduates have gone on to work in:

• software engineering
• mobile applications development
• systems analysis
• consultancy
• networking
• web design and e-commerce
• finance and insurance
• commerce
• engineering
• education
• government
• healthcare

Recent graduates have gone on to develop successful careers at leading companies such as:

• BAE Systems
• Cisco
• IBM
• The Walt Disney Company
• Citigroup
• BT

Help finding a job

The University has a friendly Careers and Employability Service, which can give you advice on how to:

• apply for jobs
• write a good CV
• perform well in interviews.

You will have access to a dedicated Employability Coordinator who is a useful contact for all student employability queries.

Career-enhancing skills

You graduate with a solid grounding in the fundamentals of data science and a range of professional skills, including:

• programming
• modelling
• design.

To help you appeal to employers, you also learn key transferable skills that are essential for all graduates. These include the ability to:

• think critically
• communicate your ideas and opinions
• analyse situations and troubleshoot problems
• work independently or as part of a team.

You can also gain extra skills by signing up for one of our Kent Extra activities, such as learning a language or volunteering.