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BSc (Hons)
Data Science
Key Information
Academic Year
Develop the skills to analyse data and solve real-world problems
Data Science is about using mathematics, statistics, programming, and computational techniques to find patterns in data and turn them into useful insights. On this BSc, you'll learn how data can be collected, analysed, modelled, visualised, and used to solve problems across areas such as healthcare, finance, business, engineering, agriculture, and public services.
This degree has a strong mathematical focus, combining topics such as calculus, linear algebra, probability, statistics, mathematical modelling, and optimisation with programming, data science, machine learning, and artificial intelligence. This combination is designed to give you both the mathematical understanding and practical skills needed to work with increasingly complex datasets.
You'll apply what you learn to real-world problems through individual and group projects, practical workshops, case studies, and project-based assessments. You'll also have opportunities to develop professional skills and gain additional industry-recognised certifications in areas such as Data, AI, Security, and Cloud.
Why study Data Science at Lincoln?
Data Science sits between mathematics, statistics, and computing. At Lincoln, you’ll develop a deeper understanding of the mathematical ideas that underpin data analysis, rather than simply learning how to use data tools.
Learn to apply data science to real problems
The course is designed around the application of knowledge, not just theoretical study. You’ll work with mathematical models and computational packages to investigate data-intensive problems, with teaching supported by real-world case studies, practical workshops, and individual and group projects.
Develop evidence-based professional skills
Data scientists need more than technical knowledge. You’ll develop your ability to communicate findings, work collaboratively, solve unfamiliar problems, and make informed decisions using evidence.
Add professional certifications to your degree
You’ll have the opportunity to access free Professional Certifications in areas including Data, AI, Security, and Cloud. These include Microsoft Fundamentals and Pearson IT Practitioner credentials, giving you additional evidence of your practical skills when applying for placements and graduate roles.
Choose a placement year
You can apply to spend a year in industry between your second and third years. This gives you the opportunity to put your learning into practice, gain professional experience, and develop a clearer understanding of the type of data career you want to pursue. You can also pursue shorter placements, such as summer internships, or contribute to academic research projects.
Modules
Module Overview
This module focuses on the concepts of the derivative and the Riemann integral, which are indispensable in modern sciences. Two approaches are used: both intuitive-geometric, and mathematically rigorous, based on the definition of continuous limits. Important results are the Mean Value Theorem, leading to the representation of some functions as power series (the Taylor series), and the Fundamental Theorem of Calculus which establishes the relationship between differentiation and integration. Further calculus tools are explored, such as the general properties of the derivative and the Riemann integral, as well as the techniques of integration. In this module, students may deal with many "popular" functions used throughout mathematics.
Module Overview
This module introduces core data technologies and practices. Students will learn the principles of SQL and relational databases, explore NoSQL and non‑relational data models, and gain an understanding of knowledge graphs and ontologies for semantic data representation. Students will develop practical skills in data joining and transformation, data warehousing concepts, and creating effective data visualisations.
Module Overview
Data science is a field of study that utilises algorithms, statistics, and visualisation methods to answer scientific questions using data. In this module, students will learn how to load, transform, visualise, and extract knowledge from data using their skills as programmers. Students will also gain experience in using interactive programming environments (e.g. IPython/Jupyter) and open-source libraries (e.g., numpy, matplotlib, pandas) that are widely by data scientists in industry. During the module, students will work in groups to analyse a real-world dataset and present their findings to their peers.
Module Overview
This module provides students with a practical introduction to machine learning. Students will learn how classification, regression, clustering, and time‑series analysis techniques can be applied to solve real-world problems. Alongside traditional machine learning, Generative AI tools will be introduced. Students will investigate the capabilities and limitations of generative models, considering issues such as bias and sustainability. Students will begin developing skills in prompt‑driven development—learning how to design effective prompts and assess AI‑generated outputs.
Module Overview
This module describes vector spaces and matrices. Matrices are regarded as representations of linear mappings between vector spaces. Eigenvalues and eigenvectors are introduced, which lead to diagonalisation and reduction to other canonical forms. Special types of mappings and matrices (orthogonal, symmetric) are also introduced.
Module Overview
This module begins with an introduction of a probability space, which models the possible outcomes of a random experiment. Basic concepts such as statistical independence and conditional probability are introduced, with various practical examples used as illustrations. Random variables are introduced, and certain well-known probability distributions are explored.
Further study includes discrete distributions, independence of random variables, mathematical expectation, random vectors, covariance and correlation, conditional distributions and the law of total expectation. The ideas developed for discrete distributions are applied to continuous distributions.
Probability theory is a basis of mathematical statistics, which has so many important applications in science, industry, government and commerce. Students will have the opportunity to gain a basic understanding of statistics and its tools. It is important that these tools are used correctly when, for example, the full picture of a problem (population) must be inferred from collected data (random sample).
Module Overview
This module provides students the opportunity to learn a variety of transferable skills: to communicate scientific ideas via a variety of media, to work in groups, to manage and plan projects, to keep record of work.
Students have the opportunity to develop an understanding of general and specialized databases, their uses and searches. Group study can develop Students' skills in team-working around investigating a topic from literature. Students have the opportunity to take on administrative roles within the team and work towards common aims and objectives.
Module Overview
This module provides an essential introduction to software development, guiding students through the core principles of programming using a high‑level language. Students will progress from foundational concepts such as variables, iteration, and conditional logic to building structured programs that incorporate methods, objects, and classes. Through hands‑on workshop activities, students will gain practical skills in software development.
Module Overview
This module offers a hands‑on introduction to the core concepts of machine learning, showing you how intelligent systems learn from data to make predictions and uncover hidden patterns. Through practical work with both supervised and unsupervised methods, you will build the skills needed to tackle real‑world data science challenges and apply machine learning techniques across a wide range of domains.
Module Overview
The module aims to provide a modern introduction to the concepts of symbolic artificial intelligence, set in the context of intelligent agents.
The module covers the concepts such as state space representations and search, heuristic and adversarial search methods, and optimization techniques. The module also covers knowledge representation, AI planning, and some nonstatistical, machine learning methods.
Module Overview
Organisations produce and process vast amounts of data to gain strategic insights. This module introduces students to the essential principles of data governance and business intelligence, focusing on the frameworks and practices used across industry to ensure data is trustworthy, secure, and ready to support decision‑making. Through case study analysis, students will learn about the data integrity, ethical and sustainable challenges faced by organisations.
Module Overview
Calculus techniques already provide solutions of simple first-order differential equations. Solution of second-order differential equations can sometimes be achieved by certain manipulations. Students may learn about existence and geometric interpretations of solutions, even when calculus techniques do not yield solutions in a simple form. This is a part of the existence theory of ordinary differential equations and leads to fundamental techniques of the asymptotic and qualitative study of their solutions, including the important question of stability. Fourier series and Fourier transform are introduced.
This module provides an introduction to the classical second-order linear partial differential equations and techniques for their solution. The basic concepts and methods are introduced for typical partial differential equations representing the three classes: parabolic, elliptic, and hyperbolic.
Module Overview
This module aims to provide students with the experience of working as part of a team on a project.
Students will have the opportunity to produce a set of deliverables relevant to their programme of study. Final deliverables will be negotiated between the group and their supervisor, the module coordinator will be responsible for ensuring that each project covers the learning outcomes of the module. Groups are expected to manage their own processes, and to hold regular meetings both with and without their supervisor. Groups will be allocated by the module coordinator and other members of staff. The process of development of the topic under study and the interaction and management of group members underpins the assessment of skills in the module.
Module Overview
Students have the opportunity to learn how mathematics is applied to modern industrial problems, and how the mathematical apparatus finds applications in the financial sector.
Module Overview
In this module, students learn how computers can be used to analyse and process the natural language that we use in our everyday lives. Natural language is a data type like no other, and presents a unique set of challenges for which the field of Natural Language Processing (NLP) has sought to provide answers. Common applications of NLP include machine translation, text summarisation, question answering, chatbots, grammar checking, and many others.
Module Overview
This module provides students with the opportunity to develop knowledge of the processes and principles of Human-Computer Interaction (HCI) and User Experience Design (UXD) starting with a history and overview of the role HCI in furthering the field of computer science. The module will guide students through notions of usability and accessibility, user-centred design and requirements analysis, prototyping, statistical analysis, and qualitative evaluation using state of the art methods and techniques. The professional, ethical, social, and legal issues in designing and studying interactive technology will be considered throughout.
Module Overview
This module equips students with the skills needed to take data‑driven solutions from prototype to production. Students will learn how to deploy, monitor, and maintain models in operational environments, exploring both batch and real‑time deployment pipelines, cloud‑based deployment strategies, and the principles of MLOps. Students will gain hands-on experience of producing data visualisations for monitoring model performance.
Module Overview
Inspired by the biological neurons that make up our brains, artificial neural networks (ANNs) are simple mathematical models that date back to the work of McCulloch and Pitts in the 1940s. Today, ANNs are the powerhouses of modern AI solutions and are regarded as one of the most important technical innovations of the past decade. In this module, we will follow the chronology of the deep learning revolution, starting with the basics of deep feed-forward networks and how to train them effectively. We will then work our way forwards in time and study convolutional neural network (CNN) architectures for images, recurrent neural networks (RNN) for time series data, and unsupervised models for representation learning. Towards the end of the module, students explore how deep nets learn and study the issues that can impact their real-world utility and the implications for society at large.
Module Overview
This module introduces key analytical techniques widely used in applied mathematics. It focuses on powerful methods for solving differential and integral equations, as well as the variational principles that underpin many scientific problems. Emphasis is placed on both theoretical understanding and practical problem-solving.
Module Overview
The module aims to equip students with knowledge of various numerical methods for solving applied mathematics problems, their algorithms and implementation in programming languages.
Module Overview
This module introduces the techniques of operational research - the mathematics of organisation. Students will learn a variety of optimisation techniques and will use these to solve applied problems, for example in transport and logistics. They will interpret the solutions, assess the advantages and disadvantages of different techniques and learn how to adapt a solution in response to new information.
Module Overview
This module aims at exposing students to modern research practices by having them engage with two facets of modern research:
1. Research awareness: By following research seminars in mathematics accurately reporting on their scientific content in a suitable writing style, and demonstrate awareness of scientific quality standard processes by critically reviewing a research article.
2. Individual research project: By undertaking an individual research project under the supervision of a member of staff. The project can be undertaken at an external collaborating establishment. Projects will be offered to students in a wide range of subjects, assigned with consideration of a students' individual preferences and programme of their studies. It provides students with an opportunity to demonstrate their ability to work independently on an in-depth project with a computer implementation element of mathematically relevant problem. Students will normally be expected to demonstrate their ability to apply practical and analytical skills, innovation and/or creativity, and to be able to synthesise information, ideas and practices to provide a problem solution.
† Some courses may offer optional modules. The availability of optional modules may vary from year to year and will be subject to minimum student numbers being achieved. This means that the availability of specific optional modules cannot be guaranteed. Optional module selection may also be affected by staff availability.
What you'll learn
You’ll build your knowledge progressively, moving from mathematical and computational foundations towards more advanced data science techniques. Mathematical foundations You’ll study the mathematics that allows data scientists to understand patterns, model problems, assess uncertainty, and develop reliable solutions.
This includes areas such as:
- Calculus
- Linear algebra
- Probability
- Statistics
- Mathematical modelling
- Numerical methods
- Optimisation
- Applied mathematics
These skills can help you understand not only what a model produces, but why it produces those results.
Programming and data science
You’ll develop programming and computational skills alongside your mathematical knowledge. You’ll explore how data can be processed, analysed, modelled, and communicated, while learning to approach problems systematically.
As you progress, you’ll encounter areas including:
- Data analysis and statistical computing
- Algorithms and data structures
- Data mining
- Artificial intelligence
- Machine learning
- Neural networks
- Data visualisation
- Computational intelligence
- Time-series analysis
- Optimisation for machine learning
The programme also covers areas such as natural language processing, scientific computing, operational research, decision analysis, and applications of probability and statistics.
Apply your knowledge through projects
Projects give you the opportunity to bring different areas of your learning together. You’ll work with data, mathematical models, and computational tools to investigate problems, interpret results, and communicate your conclusions.
Assessment is designed to test how you apply your knowledge through coursework, presentations, project reports, posters, and in-class tests.
Support and student experience
Moving into a technical degree can feel challenging, particularly when you’re learning new mathematical and programming concepts at the same time. The course is designed to build your knowledge progressively, giving you opportunities to practise skills and apply them before moving on to more advanced areas.
Teaching includes lectures, seminars, tutorials, practical workshops, and online learning resources. You’ll also work individually and collaboratively, helping you develop the communication and teamwork skills needed beyond university.
You’ll have access to on-campus computing facilities, high-performance CPU and GPU resources, library and learning resources, and careers support. The course also provides opportunities to learn from academic research, industry engagement, alumni, and placement experiences, helping you understand how Data Science is used beyond the classroom.
Careers and future opportunities
Data Science graduates can work wherever organisations need people who can understand complex information, identify patterns, and use evidence to support decisions. Depending on your interests, potential career paths include:
- Data scientist
- Data analyst
- Business analyst
- Machine learning engineer
- Data and business intelligence specialist
- Demand and forecasting analyst
- Statistical analyst
- AI-related roles
- Data science consultant
- Research and analytical roles
You could work across sectors including:
- Technology
- Healthcare
- Finance and banking
- Engineering
- Manufacturing
- Agriculture and agri-tech
- Business and consultancy
- Education
- Government and public services
The University has identified strong links with regional industries including agri-tech, healthcare, and manufacturing, where organisations increasingly use data-driven approaches to improve processes and decision-making.
Gain experience before you graduate
Employers increasingly value graduates who can demonstrate how they have applied their knowledge. You’ll have opportunities to develop this experience through projects, employer engagement, professional certifications, internships, research activity, and an optional placement year. The University’s Careers and Employability service will also work with academics to embed career development within the programme, while employers, industry delegates, placement students, and alumni can contribute to the student experience through talks and other activities.
Continue your studies
The degree can also provide a foundation for postgraduate study and research in areas such as Data Science, mathematics, computer science, artificial intelligence, and related disciplines.
Entry Requirements 2027-28
United Kingdom
104 to 112 UCAS Tariff points from a minimum of 2 A Levels or equivalent Level 3 qualifications.
If you are eligible for a contextual offer, a one grade or 8 UCAS Tariff point reduction to the standard entry requirements will be applied.
A Level: BBC
BTEC Extended Diploma: DMM
T Level: Merit
Access to Higher Education Diploma: 45 Level 3 credits with a minimum of 112 UCAS Tariff points.
International Baccalaureate: 29 points overall
GCSEs: Minimum of three at grade 4 or above, which must include English and Maths . Equivalent Level 2 qualifications may be considered.
The University accepts a wide range of qualifications as the basis for entry and do accept a combination of qualifications which may include A Levels, BTECs, Extended Project Qualification (EPQ).
We will also consider applicants with extensive and relevant work experience and will give special individual consideration to those who do not meet the standard entry qualifications.
International
Non UK Qualifications:
If you have studied outside of the UK, and are unsure whether your qualification meets the above requirements, please visit our country pages for information on equivalent qualifications.
https://www.lincoln.ac.uk/studywithus/internationalstudents/entryrequirementsandyourcountry/
International students will be required to demonstrate English language proficiency equivalent to IELTS 6.0 overall, with a minimum of 5.5 in each element. For information regarding other English language qualifications we accept, please visit the English Requirements page.
If you do not meet the above IELTS requirements, you may be able to take part in one of our Pre-sessional English and Academic Study Skills courses.
The University of Lincoln's International College also offers university preparation courses for international students who do not meet the direct entry requirements. Upon successful completion, students can progress to Bachelor's study at the University of Lincoln. Please visithttps://www.lincoln.ac.uk/internationalcollege/ for more information.
For applicants who do not meet our standard entry requirements, our Science Foundation Year can provide an alternative route of entry onto our full degree programmes:
https://www.lincoln.ac.uk/course/sfysfyub/
If you would like further information about entry requirements, or would like to discuss whether the qualifications you are currently studying are acceptable, please contact the Admissions team on 01522 886097, or email admissions@lincoln.ac.uk.
Contextual Offers
At Lincoln, we recognise that not everybody has had the same advice and support to help them get to higher education. Contextual offers are one of the ways we remove the barriers to higher education, ensuring that we have fair access for all students regardless of background and personal experiences. For more information, including eligibility criteria, visit our Offer Guide pages. If you are applying to a course that has any subject specific requirements, these will still need to be achieved as part of the standard entry criteria.Is this course right for you?
This course could be a strong fit if you:
- Enjoy mathematics and problem-solving
- Want to understand how data can be used to solve real problems
- Are interested in programming, statistics, AI, or machine learning
- Want a Data Science degree with a strong mathematical emphasis
- Are considering careers in data, analytics, AI, finance, technology, engineering, or research
- Want opportunities to gain professional certifications and industry experience
- Are considering postgraduate study or research
You don’t need to have your future career mapped out before you start. The course is designed to give you a broad foundation across mathematics, statistics, programming, and Data Science, allowing you to develop your interests as you progress.
Fees and Funding
University Study is a major investment, so it’s important to understand the costs and support available. A full breakdown of the fees associated with this programme can be found below. Eligible students may be able to access scholarships and bursaries to help with study costs.