Data Science MSci/BSc (Hons)

Undergraduate

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Data scientists and 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

Degree with foundation year

Year of entry

2025-26 2026-27

Location

Coventry University (Coventry)

Study mode

Full-time
Sandwich

Duration

BSc:
3 years full-time
4 years sandwich
MSci:
4 years full-time
5 years sandwich

UCAS codes

256A / 257A

Start date

September 2025

How to apply

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Course overview

The course 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.

Rated Gold Overall

Teaching Excellence Framework (TEF) 2023

5 QS Stars for Teaching and Facilities

QS Stars University Ratings

Top 5 Student City in England (Coventry)

QS Best Student Cities Index 2025

Why you should study this course

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 laboratory sessions⁴ which help solidify your understanding. You will also begin developing the professional skills you will need in your day-to-day career on graduation, e.g. working as part of a team and the ethical and legal issues around data structure and models.

Modules

Calculus - 20 credits

This module provides core calculus for those undertaking degrees in the mathematics area. The primary aims of the module are 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

Algebra - 20 credits

The aim of the module is two-fold. First, to introduce you to modern algebra by taking what you have already learned 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

Programming: Concepts and Algorithms - 20 credits

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

Working with Data - 20 credits

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 twenty-first 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

Programming: Professional Practice - 20 credits

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

Probability and Statistics - 20 credits

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 your second year, you will develop more advanced knowledge and skills to do with data science, linear statistical models and artificial intelligence (AI), amongst others.

Modules

Artificial Intelligence - 20 credits

In this module, you’ll gain a comprehensive understanding of modern artificial intelligence concepts and applications. You’ll explore the differing definitions of 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

Linear Algebra and Differential Equations - 20 credits

This module continues linear algebra and differential equations from your 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 (SVD) which will bring together bits of linear algebra seen over this course's first two years. The module will also cover second order Ordinary Differential Equations (ODEs) (linear, but with non-constant coefficients), systems of first order ODEs, series solutions and self-adjoint operators.

Compulsory

Advanced Algorithms - 20 credits

Building on your programming and algorithms studies from the 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

Data Science - 20 credits

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

Linear Statistical Models - 20 credits

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

Data Science Group Project - 20 credits

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.

Modules

UK Work Placement – 0 credits

This module provides you with an opportunity to reflect upon and gain experience for an approved placement² 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

International Study/Work Placement – 0 credits

This module provides you with an opportunity to reflect upon and gain experience for an approved international study/work placement² 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 your Data Science BSc (Hons) course, you'll cover advanced topics in data science including big data management and visualisation methods, machine learning algorithms, Artificial Neural Networks (ANNs) and advanced statistical methods.

Modules

Data Visualisation - 20 credits

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

Statistical Methods for Data Science - 20 credits

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

Compulsory

Machine Learning - 20 credits

Building on your existing knowledge of AI, 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 ANN is and when best to deploy one to solve a problem. You'll also explore how to analyse the effectiveness of a wealth of machine learning algorithms, when applied to actual data.

Compulsory

Project Discovery - 20 credits

'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 students in collaboration with students at international collaborators².

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

Dissertation and Project Artefact - 20 credits

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 you to collaborate with students at international participating institutions to develop a cross-site project². You will be supported by a supervisor (two if the group option is chosen) and the module is heavily focused on self-management.

Compulsory

Optional Modules

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 (NNs) 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 and 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 progression requirements, you could choose to take an additional fourth year master's option, which will deepen your knowledge and expertise.

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

Artificial Neural Networks - 15 credits

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 and 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

Machine Learning - 15 credits

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

Natural Language Processing - 15 credits

The aim of this module is to give you a deep understanding of, and ability to use, automated methods for processing linguistic data presented in text form, simultaneously introducing the classical and advanced machine learning Natural Language Processing (NLP) solutions for a wide range of real-world applications on pure text data.

Compulsory

Big Data Analytics and Data Visualisation - 15 credits

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 the advanced analytical techniques require to understand big data will also be covered.

Compulsory

Data Management Systems - 15 credits

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

Modelling and Optimisation Under Uncertainty - 15 credits

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. Upon completion of the module, you can expect to have a good understanding of evolutionary computation techniques, and should be able to solve a wide range of real-world optimisation problems.

Compulsory

Individual Research Project Preparation - 15 credits

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

Individual Research Project - 15 credits

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

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.

Teaching contact hours

As a full-time undergraduate student, you will study modules totalling 120 credits each academic year. A typical 20 credit module requires a total of 200 hours study. This is made up of teaching contact hours, guided and independent study.

Teaching hours:

Teaching hours vary each semester, year of study and due to module selection. During your first year you can expect 15-18 teaching hours each week. You will also have the option to attend optional sessions including time with a progress coach or to meet with staff for advice and feedback. As you progress through your studies, teaching hours may reduce.

Guided and independent study:

Throughout your studies, you will be expected to spend time in guided and independent study to make up the required study hours per module. You'll be digging deeper into topics, review what you've learnt and complete assignments. This can be completed around your personal commitments. As you progress through your studies, you'll spend more time in independent study.

Online learning:

As an innovative university, we use different teaching methods including online tools and emerging technologies. So, some of your teaching hours and assessments may be delivered online.

Assessment

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

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UCAS details

1

Course name: Data Science MSci/BSc (Hons)

2

Course code: 256A / 257A

3

Course location: Coventry University (Coventry)

Typical entry requirements:

Not got the required grades? We offer this degree with an integrated foundation year.

Fees and funding

Student	Full-time	Part-time
UK, Ireland*, Channel Islands or Isle of Man	£9,535 per year	Not available
EU	£9,535 per year with EU Support Bursary £19,850 per year without EU Support Bursary	Not available
International	£19,850 per year	Not available

If you choose to study this course with a professional placement² or study abroad year, you will need to pay a tuition fee³ to cover your academic support throughout your placement year. Students commencing their professional placement in the academic year 2027/28 will pay £1,500 if they are paying UK fees, or £1,800 if they are paying international fees.

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.

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 permits 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.

If you choose to study this course with a professional placement, the University will charge the tuition fees stated above for those on a placement during Academic Year 2027/28. The University will review professional placement tuition fees each year. For UK (home) students, the University may increase fees for each subsequent year of study, but such that it will be no more than 5% above 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.

We offer a range of International scholarships to students all over the world. For more information, visit our International Scholarships page.

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.

**EU Support Bursary

Following the UK's exit from the European Union, we are offering financial support to all eligible EU students who wish to study an undergraduate or a postgraduate degree with us full-time. This bursary will be used to offset the cost of your tuition fees to bring them in line with that of UK students. Students studying a degree with a foundation year with us are not eligible for the bursary.

How do you know if you need to pay UK or international tuition fees?

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 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

Your studies will be centred in high-specification learning environments that benefit from extensive social learning facilities, well-appointed laboratories, industry-standard equipment, lecturing facilities and classrooms, facilitating our innovative teaching methods across a diverse suite of courses.

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.

The sigma centre provides a wide range of learning resources dedicated to mathematics and statistics. Students can make use of drop-in sessions or one-to-one appointments.
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

This data science course 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 may often find yourself working in a multi-disciplinary 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.

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UK students

Full-time students can apply for this course through UCAS. Read our application pages to find out your next steps to apply.

How to apply

Apply via UCAS

If this course has a part-time option you will need to apply directly to the university.

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ukadmissions@coventry.ac.uk

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International students

International codes:
BSc: EECU148
MSci: EECU149

How to apply

For further support for international applicants applying for an undergraduate degree view our International hub.

You can also download our International mini guide for an overview of study options and why you should study with us.

Get in touch with us today for further advice and guidance.

+44 (0)24 7765 2152

ioadmissions@coventry.ac.uk

Complete our contact form

Disclaimers

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.

If you choose to study this course with a professional placement, the University will charge the tuition fees stated above for those on a placement during Academic Year 2025/26. The University will review professional placement tuition fees each year. For UK (home) students, the University may increase fees for each subsequent year of study, but such that it will be no more than 5% above inflation.

⁴Facilities

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 2024/2025 contract is available on the website for information purposes however the 2025/2026 contract will apply for the 2025/2026 intake. 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.