Undergraduate BSc (Hons)
Data Science
with a Year in Industry
Learn valuable analytical skills while exploring cutting-edge mathematical science.
Learn valuable analytical skills while exploring cutting-edge mathematical science.
Data science combines powerful computing technology, sophisticated statistical methods, and expert subject knowledge to analyse and gain practical insights from huge amounts of data produced by modern societies.
Our specialist BSc Data Science programme combines the expertise of internationally-renowned statisticians and mathematicians and computer scientists and machine learners to ensure that you develop the expertise and quantitative skills required for a successful future career in the field.
Your year in industry takes place between your second and final year, giving you invaluable work experience. You earn a salary and there may be the possibility of a job with the same company after graduation.
An emerging field in recent decades, data science is now an exciting, fulfilling and high-profile career choice.
Learn industry standard software like PROPHET, R and Python.
Amazing facilities, software packages and makerspaces on a vibrant campus with stunning city views.
You’ll benefit from free membership of the Kent Maths Society and Invicta Actuarial Society.
You gain invaluable workplace experience, get paid and have the chance to assess a particular career path.
BBB
DDM
120 tariff points from your IB Diploma, including Maths at 4 at HL or SL, typically H5, H6, H6 or equivalent.
Mathematics grade 6/B
N/A
The University will consider applicants holding T level qualifications in subjects closely aligned to the course.
A typical offer would be to obtain the Access to HE Diploma in a suitable subject with a minimum of 45 credits at Level 3, with 24 credits at Distinction and 21 credits at Merit.
The following modules are what students typically study, but this may change year to year in response to new developments and innovations.
Mathematics underpins many of the methods and techniques used in data science. You’ll be equipped with the mathematical foundations necessary to excel in numerous areas in data science including data analysis, interpretation, modelling, and machine learning.
You'll develop fundamental knowledge of calculus and linear algebra and learn how to use these concepts in Python coding applications to solve challenging problems. Lectures and problem- solving classes are complemented with readings, exercise sets, worked examples, assessments, and Python code, to support your learning. Python code is used to illustrate techniques to solve equations, work with functions, and compute and apply derivatives and integrals.
On completion of the module, you’ll leave with a solid mathematical foundation, enabling you to start developing skills in mastering complex data science problems with confidence and proficiency.
What are the foundational principles and practices that all programmers need to know? Designed for beginners and assuming no experience, this module equips you with essential programming concepts and skills.
The module is a blend of theoretical instruction and hands-on exercises with the Python programming language. The skills you acquire will help you learn other programming languages such as Java and C++, to name a few.
Practical assignments and projects enable you to apply and hone your skills. Additionally, you'll learn basic debugging techniques and best practices in coding style and documentation. By the end of the module, you’ll emerge with a solid understanding of programming fundamentals, laying the groundwork for further exploration and mastery in the field of computer science.
Our lives are completely dependent on the correct operation of software: healthcare systems, banking and university student data systems, to name just a few, are all dependent on it. Writing code is relatively easy, but writing code accurately and well requires the ability to solve problems and understand how the code we write is often just a small part of a bigger system.
In this module, you’ll learn how an object-oriented approach to software development allows us to think in a particular way about solving problems. This approach increases the likelihood that our code will be well-written and reliable.
You'll also learn about abstraction and inheritance and develop a deeper practical understanding of how to program beyond simple problems.
We use large-scale web applications every day in our lives. How much do we understand about how they are structured and deployed, and how they operate? Web development technologies and frameworks change every year, while new ones are constantly proposed. As web developers, how do we cut through the marketing hype to evaluate what these really provide?
In this module you’ll examine the fundamental technologies that make modern web applications work. You’ll learn to use operating and cloud systems to deploy, configure, and monitor software. You’ll dive into networking, from the basic principles of network latency and bandwidth to addressing and transmitting at different layers, from the datalink to the HTTP application layer. You’ll also develop a foundational frontend web development skill set, learning how to structure web pages using HTML, style them using CSS, and develop interactive web pages using JavaScript.
Finally, you’ll develop an appreciation of web application architecture, from the web server to the most popular web stack frameworks. You’ll learn their principles of operation and practise their deployment on the cloud at different levels of abstraction.
In the digital age, it is becoming ever easier, and more important, to collect data to predict and inform future decisions in science and society. Applications range from analysing communities and trends on the internet, researching patients’ responses to new drugs, examining the impact of global events on the stock market, and measuring population sizes of endangered species.
Many professions require skills in extracting useful information from data and managing and presenting data accurately. You’ll learn the core methods and principles of probability theory and statistics, and gain skills in applying these methods to analyse sample data and draw inferences or generalisations.
You’ll learn how to estimate an unknown parameter’s value, how to construct a confidence interval, and how to do hypothesis testing. The statistical computing package R is used throughout the module to support your learning and illustrate the methods.
R, Python, Excel, and collaborative platforms like GitHub are essential tools for academic study and professional advancement. This module equips you with comprehensive skills in working with these platforms through a range of hands-on problems and practical assignments.
As you progress through the module, you'll delve into the intricacies of R, Python, and Excel, mastering their functionalities through real-world applications. From data analysis to visualisation and interpretation, you'll gain a holistic understanding of how these tools can be harnessed to extract insights from complex datasets and analyse complex problems.
The module has a strong emphasis on collaborative work, providing opportunities for you to engage in group projects that simulate real-world scenarios. You'll learn how to effectively collaborate with peers, manage version control, and contribute to shared repositories - an invaluable skill set in today's collaborative work environments.
Algorithms are the most essential concept in computer science. They are what allow Google to search the entirety of human knowledge in nanoseconds, answering a query for millions of users worldwide simultaneously. Algorithms are behind GPS routing software and every successful rocket launch. Algorithms are what allow our DNA to be replicated and passed on through generations.
Understanding algorithms and how to analyse them–both for correctness and efficiency–and learning how to develop new algorithms is the most crucial step in becoming a successful computer scientist.
Throughout the module you will develop the skills to read and interpret problem descriptions, and the knowledge you need to solve these problems. Your deepened understanding of algorithms from runtime, to executable programmes will set you up for an exciting and successful career in rapidly expanding digital industries.
Database systems are a cornerstone of most software. The contact list of your phone is managed by a database, as are the custom-made applications used by small and midsized enterprises, as well as the large-scale databases of internet platforms with their billions of users.
This module introduces you to the theory and practice of database systems. You’ll model, design, implement, and use database systems, gaining valuable skills you will need in your career as a software developer.
By the end of the module, you’ll be able to use query languages, emerging techniques, and future technologies in the field of database systems.
The omnipresence of Artificial Intelligence (AI) is undeniable; it is not merely a futuristic concept but a current reality that permeates almost every facet of our lives. As we witness AI's pervasive influence, it becomes evident that it is destined to be an integral part of our everyday existence.
In this dynamic module, you will be introduced to the essential concepts of AI, setting the stage for a profound exploration into more advanced realms such as machine learning and bio-inspired computations. Through engaging weekly classes, your understanding will evolve, seamlessly transitioning from foundational principles to the intricacies of advanced concepts.
To reinforce your knowledge, the classes incorporate exercise questions tailored to guide you through a comprehensive learning journey. The inclusion of hands-on coding exercises, utilising a programming AI tool, further solidifies your understanding. This practical approach not only enriches your theoretical foundation but also equips you with the skills necessary for navigating the complexities of AI development.
Emphasis on real-world considerations ensures that you emerge from this course well-prepared to tackle more advanced modules later as you will not only possess a foundational understanding of AI but also wield practical expertise essential for active participation in AI development.
Explanatory and predictive modelling is essential to data-driven decision-making. Throughout this module, you’ll learn about regression, the cornerstone of versatile statistical analysis and master diagnostics, model specification, selection, and interpretation.
Through hands-on activities and real data analysis, you’ll gain the skills to extract actionable insights and forecast future trends confidently. The module is designed to equip you with the required tools to navigate complex datasets across different areas of application and practice.
This tailored module on explanatory and predictive modelling in context will help you unlock the essence of data-driven decision-making. You’ll learn about regression, the cornerstone of versatile statistical analysis, mastering diagnostics, model specification, selection, and interpretation.
Through hands-on activities and real data analysis, you’ll gain the skills to extract actionable insights and forecast future trends confidently. The module is designed to equip you with the necessary tools to navigate complex datasets across different areas of application and practice.
Gaining work experience is vital to improving your chances of finding employment once you graduate. This module is designed to simulate real-world work experiences, where you will work in groups on open-ended projects requiring a combination of diverse skills and knowledge.
You’ll collaborate with others within set time frames to tackle authentic mathematical and data science challenges using real-world data sets, honing your ability to learn new material, combine your skills, and work effectively as a team. Topics covered involve data sets relating to current industry, societal or scientific challenges, preparing you for the complexities of working with authentic data. You’ll present your findings in various formats including presentations, posters, blog posts, and reports, allowing you to develop skills essential for your future career.
In short, you’ll have an exciting opportunity to apply your mathematics and data science skills and work in a team on practical problems from industry or science to help you develop crucial professional competencies.
You spend a year working in an industrial or commercial environment between Stages 2 and 3. Our students go to a wide range of companies including:
They have also been to overseas employers in locations including Amsterdam, Hong Kong and the US.
What could you do in a year?
My year in industry couldn’t have gone better. I secured a role at IBM, working in their sports and entertainment department – it was perfect for meTom Tillin Find out more
Natural organisms are well adapted to solve complex problems related to their survival in very dynamic and diverse environments. How do they solve such complex problems without using optimisation methods invited by humans?
In this module, you’ll learn in detail the main mechanisms used by natural organisms for adaptation and optimisation. You’ll also learn how computer scientists have developed abstractions of those mechanisms to create several adaptive, intelligent algorithms to solve difficult real-world optimisation problems.
In addition, you’ll learn how organisms and living systems can process information, typically in ways that are quite different to traditional computers. In this part of the module, you'll learn about stochastic simulation algorithms (based on statistical probabilities), which also have a wide range of applications in AI and computer science.
Finally, you’ll learn the pros and cons of different nature-inspired problem-solving and optimisation algorithms, so that you can identify the most appropriate algorithm for a given target real-world optimisation problem.
A strong grasp of statistical modelling and optimisation principles forms the bedrock of machine learning. This module covers essential and advanced topics of machine learning and deep learning, blending theory with practical computing tools, such as R and Python.
We’ll equip you with the necessary theoretical framework to navigate through complex algorithms and methodologies. You’ll explore key concepts including classification, prediction, and regression tree-based methods through engaging real-world datasets.
You’ll uncover the power of resampling techniques and support vector machines, and dive into the exciting realm of deep learning. With applications spanning biomedical statistics, finance, and insurance, this module offers a hands-on learning experience tailored to aspiring data scientists.
What are the foundational Bayesian algorithms within the realm of probabilistic machine learning? Discover cutting-edge techniques applicable across diverse domains including natural language processing, image recognition, and fraud detection.
You’ll delve into fundamental Bayesian Inference concepts, including prior and posterior distributions, Bayesian estimation, Bayes factor, model selection, and forecasting. You’ll learn various posterior sampling algorithms and see how to apply them through real-world instances in linear regression and classification.
You’ll also learn about the latest trends in the field including variational Bayes and online learning. Through a combination of lectures and practical computer-based sessions, you’ll gain hands-on experience and theoretical insights, and gain a deep understanding of probabilistic machine learning methodologies.
Propose your own project and explore the subject in depth using the data science methods that you have studied in the earlier modules. We’ll guide you through planning the project, providing you with initial comments, and then you will prepare and give a presentation describing your initial findings.
Our feedback will help you to carry out your subsequent analyses in an effective way. Throughout the project you'll be guided by a member of academic staff who will supervise your analyses based on their particular expertise. You’ll describe your work in an extended written report, which will be evidence of your abilities in data science that you can show to potential employers.
Data mining and knowledge discovery techniques are widely used in real-world applications. Examples of high-stakes applications include analysing data to decide whether or not a patient should undergo a surgery or a customer should be granted a loan or hired for a job. You’ll learn in detail how data mining algorithms work to automatically extract knowledge from data, and why these algorithms – which are based mainly on machine learning (but also on statistics) – are so important for today’s data-driven society.
You’ll learn about the broader process of knowledge discovery, including the application of data mining algorithms to real-world datasets. You’ll also prepare data for the subsequent application of a data mining algorithm and learn how to evaluate the knowledge discovered by a data mining algorithm. This module emphasises the use of techniques that learn predictive models that can be in principle interpreted by users, as opposed to machine learning techniques that learn black-box predictive models (not directly interpretable by users).
Natural language processing (NLP) is an incredibly important and valuable component of artificial intelligence, making it a fascinating and rewarding area of study for computer science students. In today's digital age, numerous technologies rely on NLP to interpret and generate human language, such as virtual assistants, search engines for the World Wide Web, and large language models and chatbots. By delving into the realm of NLP, you can gain a deeper understanding of how these cutting-edge technologies work and the significant impact they have on our daily lives. Studying NLP not only allows you to explore the intricacies of artificial intelligence but also provides you with valuable skills that are highly sought after in the tech industry.
Teaching is based on lectures, with practical classes and seminars, but we are also introducing more innovative ways of teaching, such as virtual learning environments and work-based tuition.
We provide excellent support for you throughout your time at Kent. This includes access to web-based information systems, podcasts and web forums for students who can benefit from extra help. We use innovative teaching methodologies, including BlueJ and LEGO© Mindstorms for teaching Java programming.
Our staff have written internationally acclaimed textbooks for learning programming, which have been translated into eight languages and are used worldwide.
For a student studying full time, each academic year of the programme will comprise 1200 learning hours which include both direct contact hours and private study hours. The precise breakdown of hours will be subject dependent and will vary according to modules.
Methods of assessment will vary according to subject specialism and individual modules.
Please refer to the individual module details under Course Structure.
For course aims and learning outcomes please see the course specification.
You graduate with a solid grounding in the fundamentals of data science and a range of professional skills, including:
To help you appeal to employers, you also learn key transferable skills that are essential for all graduates. These include the ability to:
You can also gain extra skills by signing up for one of our Kent Extra activities, such as learning a language or volunteering.
An industrial placement can greatly enhance your studies and have a dramatic impact on your graduate choices.
*The Government announced on 4 November 2024 that tuition fees in England for Home students will increase to £9,535 from £9,250 for the academic year 2025/26. This increase requires Parliamentary approval, which is expected to be given in early/mid 2025.
Tuition fees may be increased in the second and subsequent years of your course. Detailed information on possible future increases in tuition fees is contained in the Tuition Fees Increase Policy.
The University will assess your fee status as part of the application process. If you are uncertain about your fee status you may wish to seek advice from UKCISA before applying.
For details of when and how to pay fees and charges, please see our Student Finance Guide.
Fees for undergraduate students are £1,900.
Fees for undergraduate students are £1,430.
Students studying abroad for less than one academic year will pay full fees according to their fee status.
You will require regular access to a desktop computer/laptop with an internet connection to use the University of Kent’s online resources and systems. Please see information about the minimum computer requirements for study.
Find out more about accommodation and living costs, plus general additional costs that you may pay when studying at Kent.
Kent offers generous financial support schemes to assist eligible undergraduate students during their studies. See our funding page for more details.
We have a range of subject-specific awards and scholarships for academic, sporting and musical achievement.
Next steps
If you are from the UK or Ireland, you must apply for this course through UCAS. If you are not from the UK or Ireland, you can apply through UCAS or directly on our website if you have never used UCAS and you do not intend to use UCAS in the future.
We welcome applications from students all around the world with a wide range of international qualifications.
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