Data Science MSci/BSc (Hons) with foundation year

Study level: Undergraduate
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Data scientists/analysts are in demand across a large range of sectors, from healthcare to finance, from marketing to transport. This course aims to provide the essential training you will need to be successful in this fast-moving, dynamic field.

Course option

Year of entry

Location

CU Coventry (Coventry) and
Coventry University (Coventry)

Study mode

Full-time
Sandwich

Duration

BSc:
4 years full-time
5 years sandwich

UCAS codes

25DF

Start date

September 2024
November 2024


Course overview

Our degree with foundation year could be the stepping stone you need to achieve your goals. The foundation year aims to prepare you for degree-level study and is a great way to build the confidence, skills and knowledge needed to succeed on your degree course. The degree with foundation year is only available for the BSc option.

Foundation year

The course aims to provide you with a solid grounding in mathematical principles and an understanding of the core technology associated with the use and application of computer systems. Professional and academic skills are integrated across all modules, including information finding and handling, problem-solving and the communication of outcomes.

Degree

The degree brings together a range of techniques that the modern data scientist needs. You will study modules in mathematics, data analysis and computing and tackle a variety of interesting and engaging problems from the latest research studies, business and industry.

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Joint Top Modern University for Career Prospects

Guardian University Guide 2021 and 2022

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5 QS Stars for Teaching and Facilities

QS Stars University Ratings

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Top 5 UK Student City (Coventry)

QS Best Student Cities Index 2023

Why you should study this course

Foundation year

  • Supports you to gain the academic skills required for degree-level study.
  • Provides a grounding in key areas of computing and mathematics.

Degree

  • You will have the chance to equip yourself with transferable and professional skills which prepare you for employment in industry, business, or education.
  • You will be provided with the opportunity to develop critical and reflective skills required for problem-solving in a variety of contexts.
  • The course has a particular emphasis on modern applications and the use of appropriate computational methods, software and technology.
  • You can expect to improve your knowledge and understanding of the theory and practice of the latest data science, as well as the use of computational methods.
  • You will have the chance to gain industry-relevant experience2 as you apply real-world, commercial software development practices within teams of your peers, preparing you for your career after graduation.

What you'll study

In your first year, you will be taught the fundamental skills and concepts needed to begin your journey as a data scientist. You’ll be familiarised with the mathematical, statistical and computational foundations, and you will apply those principles in regular laboratory sessions which help solidify your understanding. You will also begin developing the professional skills you will need in your day-to-day career on graduation: working as part of a team, the ethical and legal issues around data structure and models. 

Modules

  • This module provides core calculus for those undertaking degrees in the mathematics area. The primary aims of the module is to consolidate the material covered in the different A levels and equivalent qualifications, and explore some of the advanced concepts needed for an extensive study of mathematics. 

    Compulsory

  • The aim of the module is two-fold. First, to introduce you to modern algebra by taking what you have learned at high school and placing it in the context of university mathematics. Emphasis will be given to the importance of assumption and proof in mathematics. Second, you will encounter one of the fundamental pillars of modern mathematics - linear algebra. You will see the key result of the basis theorem, as well as explore the one-to-one correspondence between linear maps and matrices over a field. This will be key for other areas of mathematics you will see in your undergraduate degree. 

    Compulsory

  • Whatever software we’re developing, we need to understand the fundamentals of programming in order to build it – that’s as true for an interactive website as it is for a smartphone app. In this module, you’ll be introduced to these fundamentals through an accessible and industry-favoured programming language. You’ll explore algorithms – what they are, why they’re important, and how to use them – and you’ll combine this with your programming skills to write your own programs. 

    Compulsory

  • Databases are fundamental to modern, digital life – whatever we’re doing, we’re either generating, using, sharing or erasing data. The technologies, ethics and laws behind these processes are a fascinating and fundamental element of software development in the 21st century. In this module, you’ll explore all of these concepts, mastering the elements of data handling, storage, and management which you’ll have to apply in later study. 

    Compulsory

  • This module builds upon and develops the fundamental computer programming skills you developed in Concepts and Algorithms. You will be introduced to new ideas such as object-orientation, and designing reusable code, and you’ll explore them using another industry-favoured programming language. You’ll be taught to structure your code in a way that makes it easy to follow, maintain, and extend, equipping you for the next stage of your software development studies. 

    Compulsory

  • This module builds a foundation for the study of statistics in future years. It will introduce you to the concepts of probability, random variables and probability distributions. It will also introduce the concepts of estimation and hypothesis testing, and will develop the necessary theory, methods and concepts for statistical analysis of data. 

    Compulsory

In year two, you will develop more advanced knowledge and skills to do with data science, linear statistical models and artificial intelligence, amongst others. 

Modules

  • In this module you’ll gain a comprehensive understanding of modern artificial intelligence concepts and applications. You’ll explore the differing definitions of just what ‘artificial intelligence’ means, and the legal and ethical issues which arise surrounding decision-making computer systems. Ultimately, you’ll build a portfolio of solutions that address artificial intelligence challenges, as you navigate areas such as knowledge representation, reasoning, and how human factors impact the field of AI. 

    Compulsory

  • This module continues linear algebra and differential equations from the first year with the overlap between the areas emphasised – in both cases, the general goal will be to explore ways in which one can find a ‘suitable basis’ for a given problem. This could include orthonormal bases, bases of eigenvectors or bases of generalised eigenvectors. It will conclude with Singular Value Decomposition which will bring together bits of linear algebra seen over the first two years. The module will also cover second order ODEs (linear, but with non-constant coefficients), systems of first order ODEs, series solutions and self-adjoint operators. 

    Compulsory

  • Building on your programming and algorithms studies from first year, this module expands your insights into advanced programming techniques and complex data structures. You’ll learn what terms such as ‘graph’ and ‘tree’ mean in computing, and how to use them in your own software development. You’ll become familiar with strategies to address the computational complexity of the problems you’re trying to solve, empowering you to write more sophisticated, and more efficient, software solutions. 

    Compulsory

  • Picking up where Working with Data left off, the Data Science module equips you with the skills and tools you need to explore the world of Big Data. Using state-of-the-art software, you’ll explore concepts such as predictive modelling, data wrangling, sampling, and analysis. You’ll also explore the complex subject of data visualisation, and how you can use visualisation techniques to make the results of your data analysis understandable to every audience. 

    Compulsory

  • This module will introduce two of the most commonly used statistical techniques, multiple regression and analysis of variance (ANOVA). The statistical inference concepts of hypothesis testing, and estimation will be extended within the statistical modelling framework. Statistical computing environments and packages will be used throughout. The methods taught are used extensively in industry, commerce, government, research and development. This module is particularly relevant for those intending to go on placement year, for graduate jobs, and for final year statistics projects. The statistics modules will build on the concepts introduced in this module. 

    Compulsory

  • You will gain experience in teamwork, cooperation, and project planning. You will also get a better understanding of topics covered earlier in the course which feeds into the creation of a real-world data science project. Throughout this module, you will develop tailored modern data science tools using “Raspberry pi” to explore and illustrate the fundamental concepts in data science, and practical skills in creating datasets for the data science approaches considered for solving the adopted real-world problems.  

    Compulsory

There’s no better way to find out what you love doing than trying it out for yourself, which is why a work placement2 can often be beneficial. Work placements usually occur between your second and final year of study. They’re a great way to help you explore your potential career path and gain valuable work experience, whilst developing transferable skills for the future.

If you choose to do a work placement year, you will pay a reduced tuition fee3 of £1,250. For more information, please go to the fees and funding section. During this time, you will receive guidance from your employer or partner institution, along with your assigned academic mentor who will ensure you have the support you need to complete your placement. 

Modules

  • This module2 provides you with an opportunity to reflect upon and gain experience for an approved placement undertaken during your programme. A placement should usually be at least 26 weeks or equivalent; however, each placement will be considered on its own merits, having regard to the ability to achieve the learning outcomes. 

    Optional

  • This module2 provides you with an opportunity to reflect upon and gain experience for an approved international study/work placement undertaken during your programme. A work/study placement should usually be at least 26 weeks or equivalent; however, each placement will be considered on its own merits, having regard to the ability to achieve the learning outcomes. 

    Optional

The final stage of the BSc (Hons) in Data Science covers advanced topics in data science including Big Data management and visualisation methods, machine learning algorithms, Artificial Neural Networks and advanced statistical methods. 

Modules

  • This module develops the theory and practice of data visualisation for exploration and presentation.  Principles of visual design, human perception and cognition, data storytelling, and data ethics will be applied through modern software tools to create effective visualisations of a variety of types of data. 

    Compulsory

  • This module introduces you to more advanced statistical methods/models required to analysis and model more complex data. These include Generalised Linear Models, principal components analysis, cluster analysis and graphical Bayesian networks. The module will be set within a range of real-world contexts.  

    Compulsory

  • Building on your existing knowledge of Artificial Intelligence, this module dives into the broad field of machine learning, one of the core building blocks of many AI systems and methods. You’ll learn the difference between supervised and unsupervised machine learning, what an artificial neural network is and when best to deploy one to solve a problem, and how to analyse the effectiveness of a wealth of machine learning algorithms when applied to actual data. 

    Compulsory

  • Project Discovery is a module that enables you to identify and refine a topic relevant to your degree title. You will be able to choose between an individual project or a group project, carried out as a team with other Coventry University students in collaboration with students at international collaborators.

    Whether working individually or as a member of a team, the identification of a topic will be achieved through a range of invited talks and the formation of topic study groups. Guided study will be enabled by the allocation of a project supervisor. 
    Alongside the discovery component, there will be a lecture series addressing research methods, academic writing, results evaluation, and literature reviews. 

    Compulsory

  • This is a practical module in which you will develop and deliver a primary research artefact and a dissertation. You may engage in individual development of the artefact and write an individual dissertation; or a group-developed artefact with a group-written dissertation and an individual critical evaluation and reflection. The group option will allow Coventry University students to collaborate with students at international participating institutions to develop a cross-site project. You will be supported by a supervisor (two if the group option is chosen) and the module is heavily focused on self-management. 

    Compulsory

  • Choose one of the three optional modules:

    Artificial Neural Networks - 20 Credits
    This module introduces you to other important and popular machine learning approaches that are developed by mimicking how the human brain functions. Neural networks have been successfully used in a wide range of applications including image processing, speech and natural language processing, medical diagnosis, bioinformatics and computational biology, emotion recognition, robotics, control. This module looks into the introductory concepts used in neural networks and their application to solving real-world problems.  

    Mobile Application Development - 20 Credits
    Our everyday lives have never been more integrated with our mobile devices and applications. In this module, you’ll explore everything which goes into mobile application design, from the notion of RESTful APIs to continuous integration and analytics. You’ll demonstrate your understanding by using a development kit to build a portfolio of applications for mobile platforms.

    Advanced Topics in Statistics - 20 credits
    This module introduces you to several important topics in the areas of advanced statistics. This module is useful if you are interested in a Statistics postgraduate study or in careers involving Statistics or Data Science. 

    Optional

If you meet the criteria, you could choose to take an additional fourth year master's option, which will deepen your knowledge and expertise3.  

This year provides insight into more advanced topics in data science and can act as a stepping stone to postgraduate research or further study. 

Modules

  • Neural networks (NNs) represent an important and popular machine learning approach that attempt to model how the human brain works. NNs have been successfully used in a wide range of applications including visual image processing, speech and natural language processing, medical diagnosis and prognosis, emotion recognition, bioinformatics and computational biology, robotics, control, etc. This module introduces the concepts used in neural networks and their application to solve real-world problems. The main topics covered in this course will contain biological motivations of neural networks, different approaches including the main supervised and unsupervised neural network architectures, static and temporal learning approaches, data collection and preparation methods for neural network learning, applications of neural networks, current trends and future developments. 

    Compulsory

  • This module provides you with an introduction to machine learning techniques, the associated concepts and applications. Machine learning is the process whereby systems learn by identifying structures and patterns within data. Machine learning has proved an important tool in various applications including data mining, games design, diagnosis and natural language processing. Machine learning covers a broad field of intelligent techniques and their associated algorithms, such as data preparation and pre-processing, reinforcement learning, supervised and unsupervised learning, classification and clustering, applications of machine learning and future developments. 

    Compulsory

  • Information retrieval is among core knowledge driving some of the world’s successful and high-tech businesses including Google, Facebook, and Twitter. In this module, you will be exposed to a range of common information retrieval methods, from theory to practice. The modules main emphasis is text retrieval, covering only a brief outline of multimedia information retrieval. In addition, it emphasises data mining methods, especially for text classification and document clustering problems. 

    Compulsory

  • This module introduces you to the current management and visualisation methods for Big Data. Cutting-edge techniques will be taught which will enable you to discover patterns, relationships and associations in big data sets. You will engage with the emerging critical issues within the context of traditional database management systems which make them unsuitable to process big data. Thus, the nature of big data, recognised by its volume, velocity and variety, which prevents analysis in the normal setting of a traditional database will be studied and advanced analytical techniques require to understand big data will be covered. 

    Compulsory

  • This module provides you with a sound knowledge of the theoretical and practical underpinnings of data management systems in centralised and distributed environments. This module is motivated by the need to harness the potential of traditional systems and of modern schemes, which arose in response to the challenges posed by Big Data. The organisation and delivery of the module implements a two-pronged approach, through the investigation of data models and through the study and application of relational databases and NoSQL databases. The study of distributed frameworks such as Hadoop, and relevant distributed techniques such as sharding and replication, will provide further enhancement to the scope of the module. 

    Compulsory

  • This module provides you with more advanced concepts in machine learning, focusing on statistical methods for supervised and unsupervised learning. The module is structured around recent developments on Gaussian processes for regression and classification, Latent Dirichlet Allocation models for unsupervised text modelling and topic modelling, and Probabilistic graphical models as a powerful framework for representing complex domains using probability distributions. In addition, this module will also provide you with the advanced concepts and techniques of evolutionary computation and fuzzy logic from an application-oriented viewpoint, where worked examples and case studies will be used to reinforce the taught material. Applications of fuzzy systems will be taught in combination with other machine learning techniques within a hybrid intelligent system. On completion of the module, you will also have a good understanding of evolutionary computation techniques and should be able to solve a wide range of real-world optimisation problems. 

    Compulsory

  • This module prepares you to conduct your own research in the field of data science, under guidance from a supervisor. The exact details will depend on your preference and supervisor research interests, but this will generally include implementing or extending ideas from current research. In the preparatory module you will identify a suitable topic of study and a project supervisor. You will then exercise and extend your skills in gathering, understanding and critically evaluating literature; assessing and acting on relevant ethical and legal issues; and applying planning processes for the undertaking of a significant piece of work. 

    Compulsory

  • In this practical module, you will build on the planning and literature survey from the preparatory module and conduct the proposed research.  You will implement the necessary tools, performing the research experiments, analyse the findings, and draw suitable conclusions. Finally, you will write up your work into a significant report. 

    Compulsory

The foundation year offers an introduction to your chosen subject and supports you to develop the skills required for degree-level study.

Modules

  • This module aims to provide you with the fundamental mathematical knowledge and skills required to solve relevant problems while developing analytical and problem-solving skills. You should also be equipped to turn real-world problems into mathematical problems and present mathematical and logical arguments. Concepts covered include basic algebraic properties, trigonometry, computation of areas and volumes of basic shapes, and an introduction to Calculus including computation of limits derivatives and integrals. Upon successful completion of the module, you will have an awareness of many of the mathematical techniques required to tackle everyday problems in related disciplines.

    Compulsory

  • This module aims to provide you with the fundamental mathematical knowledge and skills required to solve relevant problems while developing analytical and problem-solving skills. You should also be equipped to turn real-world problems into mathematical problems and present mathematical and logical arguments. Concepts covered include computation of areas and volumes of basic shapes, complex numbers, application of matrices and determinants, application of vectors, application of differentiation and integration, and use of computer software to solve a scientific problem. Upon successful completion of the module, you will have an awareness of many of the mathematical techniques required to tackle everyday problems in related disciplines.

    Compulsory

  • This module aims to provide you with an understanding of fundamental software engineering and data storage concepts. Upon successful completion of the module you will leave with experience of working with interconnected software systems and sufficient knowledge of databases and a programming language. During the module assessment you will be required to create an application to meet a specified brief, as a result you will develop skills in requirements elicitation, documentation, and software and database design. You will also be assessed on your ability to read and understand code and recall programming principles from your body of knowledge.

    Compulsory

  • In this module, you will learn about the key concepts and techniques in data visualisation and understand the importance of data visualisation as a vital instrument in many disciplines. This module should enable you to graphically represent data in an easily understood format which can be effective in helping to make informed, data-driven, decisions. You will use a range of state-of-the-art data visualisation tools and technologies to communicate information both efficiently and effectively. Upon successful completion of the module you should have the knowledge and skills required to implement data visualization processes on a given dataset to better understand trends, outliers, and patterns within datasets and to solve real-world problems. You should then be able to interpret and evaluate the results effectively.

    Compulsory

We regularly review our course content, to make it relevant and current for the benefit of our students. For these reasons, course modules may be updated.


How you'll learn

Learning will be facilitated through a variety of methods which may include lectures, seminars, lab, workshops, online activities and group work.

Our teaching methods are varied, offering a number of teaching styles to suit the needs of all our students, so in addition to lectures, we also use tutorials, online support/learning, workshops and group work.

Progression through the modules develops knowledge and skills, including communication (written and oral), study skills, research methods, programming, data management, presentation and career development. We will also encourage you to consider your employability and/or entrepreneurial development.


Teaching contact hours

The number of full-time contact hours may vary from block to block, however, on average, it is likely to be around 20 contact hours per week. The contact hours may be made up of a combination of face-to-face teaching, individual and group tutorials, laboratory practicals4 and online support sessions/classes.

Additionally, you will be expected to undertake significant self-directed study of approximately 30 hours each week, depending on the demands of individual modules.


Assessment

This course will be assessed using a limited variety of methods to allow you to focus on what you are assessed on, not how. Assessment methods will consist of either a portfolio artefact with supportive report or a time-constrained phase test. Assessment takes place at a single stage at the end of each module.

You will be expected to engage in both class and online activities, and discussions. You can also expect to participate in additional guided reading and self-directed study to reinforce the learning gained from timetabled sessions.

Formative feedback will be used to prepare you for summative assessment and give you an early indication of your progress. A portion of each module’s contact time will be dedicated to course support sessions. The course support sessions2 are where you can explore areas of the course that you may find challenging or get support with your personal projects and employability efforts.


Teaching contact hours

We understand that everyone learns differently, so each of our courses will consist of structured teaching sessions, which can include:

  • On campus lectures, seminars and workshops
  • Group work
  • Self-directed learning
  • Work placement opportunities2.

The number of full-time contact hours may vary from semester to semester, however, on average, it is likely to be around 20 contact hours per week in the first and second year dropping to around 12 contact hours per week in the third and final year as you become a more independent learner.

Additionally, you will be expected to undertake significant self-directed study of approximately 20 hours each week, depending on the demands of individual modules.

As an innovative and enterprising institution, the University may seek to utilise emerging technologies within the student experience. For all courses (whether on-campus, blended, or distance learning), the University may deliver certain contact hours and assessments via online technologies and methods.

Since COVID-19, we have delivered our courses in a variety of forms, in line with public authority guidance, decisions, or orders and we will continue to adapt our delivery as appropriate. Whether on campus or online, our key priority is staff and student safety.


Assessment

This course will be assessed using a variety of methods which will vary depending upon the module. Assessment methods include:

  • Formal examinations
  • Phase tests
  • Essays
  • Group work
  • Presentations
  • Reports
  • Projects
  • Coursework
  • Exams
  • Individual assignments

The Coventry University Group assessment strategy ensures that our courses are fairly assessed and allows us to monitor student progression towards achieving the intended learning outcomes.


Entry requirements

Typical offer for 2024/25 entry.

80 UCAS tariff points. All foundation courses require 5 GCSEs at A-C/4-9 including Maths and English, and at least one A2 level or a BTEC equivalent qualification.

If you don’t fulfil the entry criteria your application may be considered on an individual basis, taking into account any work experience, other qualifications and/or any training you have completed. Speak to one of our advisers today to find out how we can help you.

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Are you eligible for the Fair Access Scheme?

We believe every student should have the opportunity to dream big, reach their potential and succeed, regardless of their background. Find out more about our Fair Access Scheme.

Got higher grades? Have you considered direct entry to the degree without foundation year?


Fees and funding

2024/25 tuition fees.

Foundation year

Student Full-time Part-time
UK, Ireland*, Channel Islands or Isle of Man £7,950 Not available
International/EU Not currently available*** Not available

Degree

Student Full-time Part-time
UK, Ireland*, Channel Islands or Isle of Man £9,250 per year Not available
International/EU Not currently available*** Not available

Please note: UK (home) tuition fees for the degree course years will be charged at the current Coventry University UK (home) degree fee level. This was set at £9,250 for the 23/24 academic year.

If you choose to do a work placement2, you should consider travel and living costs to cover this. There is also a tuition fee3 of £1,250 that will cover your academic support throughout your placement year.

For advice and guidance on tuition fees and student loans visit our Undergraduate Finance page and see The University’s Tuition Fee and Refund Terms and Conditions.

Tuition fees cover the cost of your teaching, assessments, facilities and support services. There may be additional costs not covered by this fee such as accommodation and living costs, recommended reading books, stationery, printing and re-assessments should you need them. Find out what's included in your tuition costs.

The following are additional costs not included in the tuition fees:

  • Any optional overseas field trips or visits: £400+ per trip.
  • Any costs associated with securing, attending or completing a placement (whether in the UK or abroad).

*Irish student fees

The rights of Irish residents to study in the UK are preserved under the Common Travel Area arrangement. If you are an Irish student and meet the residency criteria, you can study in England, pay the same level of tuition fees as English students and utilise the Tuition Fee Loan.

**This course with foundation year is not currently available to international students. If you do not meet the entry requirements to directly join year 1 of the degree, please take a look at our International Pathways Programme for additional options.

  • We carry out an initial fee status assessment based on the information you provide in your application. Your fee status determines your tuition fees, and what financial support and scholarships may be available to you. The rules about who pays UK (home) or international (overseas) fees for higher education courses in England are set by the government's Department for Education. The regulations identify all the different categories of student who can insist on paying the home rate. The regulations can be difficult to understand, so the UK Council for International Student Affairs (UKCISA) has provided fee status guidance to help you identify whether you are eligible to pay the home or overseas rate.

    If you meet all the criteria required by any one category, including any residence requirements, your institution must charge you the home rate. You only need to find one category that you fit into.


Facilities

Our foundation years are taught at CU Coventry’s Mile Lane building, a short walk from the city centre. You’re part of the university from day one, so during your foundation year you’ll have access to the wider facilities at Coventry University. Once you successfully complete your foundation year, you'll transfer over to studying your chosen degree at Coventry University, where you'll be taught on campus in the relevant academic buildings.

Located on our Mile Lane campus, you will have access to our Library and Learning Services (LLS), fully equipped seminar rooms and IT suites4. You can also take advantage of reading rights in Coventry University’s Lanchester Library, make use of sport centre facilities and receive full membership to Your Students' Union.

Two students walking outside with the CU Coventry building behind them.

Mile Lane

The campus is home to an on-site library with bookable one-to-one academic writing service and library support sessions, fully equipped seminar rooms, open-access study areas, a café and an IT suite. Our labs contain industry-standard equipment so that you learn using the same equipment as many industry professionals.

A student working in a booth in The Hub.

The Hub

At The Hub you'll find the Health and Wellbeing Centre, the Students’ Union and Square One (which provides entertainment from quiz nights to live music), the Spirituality and Faith Centre, Tank Studio, Careers Office and a fantastic food court.

External view of the Lanchester Library.

Lanchester Library

You will have full reading rights in Coventry University’s Lanchester Library. The library is open 24/7, 364 days a year and has many study spaces, including group and silent areas. It also currently offers touchdown computers and free-to-loan laptops.

On-site facilities4 offer you a variety of learning environments, which benefit from extensive social learning facilities, well-appointed laboratories, lecturing facilities and classrooms, facilitating our innovative teaching methods across a diverse suite of undergraduate and postgraduate courses.

  • sigma Centre: mathematics and statistics support
    An award-winning support centre which provides a range of learning resources in mathematics and statistics. You can make use of drop-in sessions or one-to-one appointments (subject to availability). 
  • Informal Study Areas: 
    Informal computer access to all the specialist software required for your studies. There are bookable spaces where you can meet with academics or work in small groups (subject to availability). 

Careers and opportunities

The Data Science degree at Coventry University is focused on preparing you to work in one of the most dynamic fields in the digital age, with a focus on transferrable and work-ready skills.

As a data analyst, you will often find yourself working in a multidisciplinary environment, as you bring your technical skills and knowledge to bear upon a specific business issue. This is precisely the type of situation upon which Coventry University built this course, aiming to equip you for the world of work. 

Data analysts have job prospects in areas such as business analysis, risk analysis, energy demand forecasting, health analytics, sports analytics, web analytics, games data analytics, social media analytics and more. 

Where our graduates work

Previous students have found employment as Financial Analysts at IBM, Gaming Financial Analysts for Warner Bros, Finance Assistants at Scottish Power, Business Performance Process Analysts at National Grid, Power Analysts at E.ON and Customer Service Analysts for Cummins.

Recent graduates have embarked on Finance Graduate Schemes, as Customer Services Analysts, Graduate Actuary, Information Analysts and a Trainee Accountants for companies like E.ON, National Grid, Thames Water, NHS, Hodge Lifetime Solutions and Prime Accountants. Others have also used their qualifications to progress into teaching careers, as well as postgraduate study to obtain MSc, MPhil and PhD qualifications. 

Further study

If you meet the criteria, you could choose to take an additional fourth year master's option3, which will deepen your knowledge and expertise. 


How to apply

  • Study location

    The Foundation Year study route will be delivered by CU, part of the Coventry University Group, for and on behalf of Coventry University.

    If you choose to study at CU Coventry for your Foundation Year, then your learning will be based at CU Coventry. Mile Lane, Coventry. Subject to meeting requirements you will then transition to the relevant Coventry University subject faculty building for your progression degree.

    Coventry University together with Coventry University London, Coventry University Wrocław, CU Coventry, CU London, CU Scarborough, and Coventry University Online come together to form part of the Coventry University Group (the University) with all degrees awarded by Coventry University. 

    1Accreditations

    The majority of our courses have been formally recognised by professional bodies, which means the courses have been reviewed and tested to ensure they reach a set standard. In some instances, studying on an accredited course can give you additional benefits such as exemptions from professional exams (subject to availability, fees may apply). Accreditations, partnerships, exemptions and memberships shall be renewed in accordance with the relevant bodies’ standard review process and subject to the university maintaining the same high standards of course delivery.

    2UK and international opportunities

    Please note that we are unable to guarantee any UK or international opportunities (whether required or optional) such as internships, work experience, field trips, conferences, placements or study abroad opportunities and that all such opportunities may be unpaid and/or subject to additional costs (which could include, but is not limited to, equipment, materials, bench fees, studio or facilities hire, travel, accommodation and visas), competitive application, availability and/or meeting any applicable travel, public authority guidance, decisions or orders and visa requirements. To ensure that you fully understand any visa requirements, please contact the International Office.

    3Tuition fees

    The University will charge the tuition fees that are stated in the above table for the first Academic Year of study. The University will review tuition fees each year. For UK (home) students, if Parliament permit an increase in tuition fees, the University may increase fees for each subsequent year of study in line with any such changes. Note that any increase is expected to be in line with inflation.

    For international students, we may increase fees each year, but such increases will be no more than 5% above inflation. If you defer your course start date or have to extend your studies beyond the normal duration of the course (e.g. to repeat a year or resit examinations) the University reserves the right to charge you fees at a higher rate and/or in accordance with any legislative changes during the additional period of study.

    4Facilities

    Facilities are subject to availability. Access to some facilities (including some teaching and learning spaces) may vary from those advertised and/or may have reduced availability or restrictions where the university is following public authority guidance, decisions or orders.

    Student Contract

    By accepting your offer of a place and enrolling with us, a Student Contract will be formed between you and the university. A copy of the current 2023/2024 contract is available on the website for information purposes however the 2024/25 Contract is currently being updated so please revisit this page before submitting your application. The Contract details your rights and the obligations you will be bound by during your time as a student and contains the obligations that the university will owe to you. You should read the Contract before you accept an offer of a place and before you enrol at the university.

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