Data Science MSci/BSc (Hons)
UCAS Code: BSc: 256A
UCAS Code: MSci: 257A
International Code: BSc: EECU148
International Code: MSci: EECU149
Coventry University (Coventry)
BSc:3 years full-time4 years sandwich
MSci:4 years full-time5 years sandwich
Study level: Undergraduate
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.
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.
Global ReadyAn international outlook, with global opportunities
Teaching excellenceTaught by lecturers who are experts in their field
EmployabilityCareer ready graduates, with the skills to succeed
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 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.
Calculus - 20 credits
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.
Algebra - 20 Credits
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.
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.
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 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.
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.
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.
In year two, you will develop more advanced knowledge and skills to do with data science, linear statistical models and artificial intelligence, amongst others.
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 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.
Linear Algebra and Differential Equations - 20 Credits
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.
Advanced Algorithms - 20 Credits
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.
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.
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.
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.
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.
UK Work Placement – 0 credits
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.
International Study/Work Placement – 0 credits
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.
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.
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.
Statistical methods for Data Science - 20 Credits
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.
Machine Learning - 20 Credits
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.
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 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.
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 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.
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.
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.
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, 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.
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.
Information Retrieval - 15 Credits
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.
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 advanced analytical techniques require to understand big data will be covered.
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.
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. 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.
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.
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.
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.
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.
In response to the COVID-19 pandemic, we are prepared for courses due to start in or after the 2023/2024 academic year to be delivered in a variety of forms. The form of delivery will be determined in accordance with Government and Public Health guidance. Whether on campus or online, our key priority is staff and student safety.
This course will be assessed using a variety of methods which will vary depending upon the module.
Assessment methods include:
- Formal examinations
- Phase tests
- Group work
- 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.
Typical offer for 2023 entry.
|Requirement||What we're looking for|
|A level||BSc (Hons): BBB to include Mathematics at grade B or above. Excludes General Studies.
MSci: ABB to include Mathematics at grade B or above. Excludes General Studies.
|GCSE||BSc (Hons)/MSci: Minimum 5 GCSEs graded 4 / C or above including English and Mathematics|
|BTEC||BSc (Hons)/MSci: Considered on an individual basis.|
|International Baccalaureate Diploma Programme||BSc (Hons): 29 points to include 5 points in Mathematics at Higher Level.
MSci: 31 points to include 5 points in Mathematics at Higher Level.
|Access to HE||BSc/MSci: Considered on an individual basis.|
We recognise a breadth of qualifications, speak to one of our advisers today to find out how we can help you.
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.
Select your region to find detailed information about entry requirements:
You can view our full list of country specific entry requirements on our Entry requirements page.
Alternatively, visit our International hub for further advice and guidance on finding in-country agents and representatives, joining our in-country events and how to apply.
English language requirements
- IELTS: 6.0 overall, with no component lower than 5.5
If you don't meet the English language requirements, you can achieve the level you need by successfully completing a pre-sessional English programme before you start your course.
For more information on our approved English language tests visit our English language requirements page.
Fees and funding
2023/24 tuition fees.
|Student||Full time||Part time|
|UK||£9,250 per year||Not available|
|International||£19,850 per year||Not available|
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.
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:
- Optional international ﬁeld trips: £400+ per trip.
- Any costs associated with securing, attending or completing a placement (whether in the UK or abroad)
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 course 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.
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
Full-time students applying to start in September 2023 can apply for this course through UCAS from 6 September 2022. Read our application pages to find out your next steps to apply.
Part-time students should apply directly to the university.
If you'd like further support or more information about your course get in touch with us today.
Full-time students applying to start in September 2023 should apply directly to the university.How to apply
For further support for international applicants applying for an undergraduate degree view our International hub.
You can also download our International guide which contains lots of useful information about our courses, accommodation and tips for travel.
Get in touch with us today for further advice and guidance.
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 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 COVID and visa requirements. To ensure that you fully understand the visa requirements, please contact the International Office.
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 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.
Facilities are subject to availability. Due to the ongoing restrictions relating to COVID-19, some facilities (including some teaching and learning spaces) may vary from those advertised and may have reduced availability or restrictions on their use.
By accepting your offer of a place and enrolling with us, a Student Contract will be formed between you and the university. The 2023/24 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.