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BSc (Hons)
Applied Data Science
Develop career-ready skills in data, AI, and machine learning
Data science is the process of collecting, managing, analysing, and interpreting data to identify patterns, answer questions, make predictions, and support better decisions. On this BSc (Hons) Applied Data Science degree at the University of Lincoln, you'll learn how data is processed and analysed, how computational and statistical techniques can uncover insights, and how those insights can be applied in real organisational contexts.
You'll develop practical skills across data analysis, programming, data engineering, machine learning, artificial intelligence, data visualisation, and cloud computing. You'll also explore how data is stored and managed, how analytical algorithms are developed and optimised, and how organisations can use data responsibly and securely. he course has a strong focus on applying what you learn. You'll work with real-world case studies, practical projects, and applied assessments designed to help you develop the technical, analytical, communication, and professional skills needed to work with data in the workplace.
The curriculum has been developed alongside data apprenticeship provision, helping to bring industry priorities into your learning. You'll also have opportunities to gain professional experience through an optional placement year, undertake shorter work experience or research projects, and access additional professional certifications in areas including Data, AI, Security, and Cloud.
Why study Applied Data Science at Lincoln?
Focus on applying data science in the real world
Develop practical skills that can be used to address organisational and business challenges, rather than studying data science purely as an academic subject.
Build skills across the data science pipeline
Learn how to source, process, clean, analyse, model, interpret, and communicate data
Develop strong computing skills
Explore programming, databases, data engineering, cloud computing, algorithms, software development, and security alongside data analysis.
Explore machine learning and AI
Learn about predictive analytics, machine learning, artificial intelligence, optimisation, automation, computer vision, and Natural Language Processing.
Work on projects throughout your degree
Project-based learning at each level allows you to apply your knowledge to practical problems and build evidence of your skills.
Understand responsible use of data
Explore data governance, security, ethics, bias, compliance, and the regulations that influence how organisations use data.
Develop business and professional skills
Learn to communicate findings effectively and understand how data can be used to improve organisational processes, operations, and decision- making.
Gain additional professional certifications
You may have the opportunity to access certifications in areas such as Data, AI, Security, and Cloud, including Microsoft Fundamentals and Pearson IT Specialist certifications.
Gain valuable industry experience
You can apply for an optional sandwich placement year, with support available throughout the process.
Modules
Module Overview
This module provides a foundational understanding of cloud computing and cybersecurity principles. Students explore core cloud concepts like virtualization, service models (IaaS, PaaS, SaaS), and deployment models (public, private, hybrid). Alongside this, they will learn about the security challenges inherent in connected systems and cloud environments, covering topics such as data security, identity and access management, compliance, and defence in depth.
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
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
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 will outline the main components of the software design and development process that ensure software is fit for purpose and of sufficient quality. Students will develop their practical understanding and appreciation of frameworks for software development processes using case studies and practical implementations.
Module Overview
In industry, computer scientists and software developers work in teams to create solutions to a variety of different problems. This module aims to introduce the art of problem solving, teamwork, and the industry employment process to help equip students with the skillsets required for an industry setting.
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
This module will explore the ‘full stack’ of web application technologies. You will have the opportunity to learn how to design and develop both the frontend and backend of modern web applications. The module aims to cover the three-tier architecture approach for developing web applications: i) presentation tier, ii) application tier, and iii) data tier. You can learn how to use the relevant technologies for each tier, encompassing web presentation, application programmable interfaces (APIs), and database technologies. The overall aim of the module is for you to learn the how to develop robust client-server applications using secure and scalable technologies.
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 purpose of this module is to provide students with the experience of working as part of a team within a simulated commercial setting. Students will go through the key phases of software development from ideation through to development, testing, delivery, and publishing. Through the module students will learn how to manage and deliver commercial software development projects. This will include ethical, social and professional issues, project management, communication, time management, and team working strategies.
This module develops on the skills learnt in the first year and places them in a simulated commercial setting. The artefact produced as part of the software development process should be suitable for inclusion within a professional portfolio.
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 provides an opportunity for students in the School of Engineering and Physical Sciences to spend a year working full-time within industry. Placements will enable students to experience the daily workings of an organisation and gain real-world experience of the concepts taught during their studies. All students on the Placement Year will remain enrolled with the University during the placement and receive support from the university.
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
Digital image processing techniques are used in a wide variety of application areas such as computer vision, robotics, remote sensing, industrial inspection and medical imaging. Image processing is the study of algorithms that take images as an input and return information about these images. This module aims to provide a broad introduction to the field of image processing, culminating in a practical understanding of how to apply and combine techniques to various image-related applications. Students will have the opportunity to extract useful data from raw images and interpret the result.
Module Overview
This module aims to equip you with the skills to design and develop connected, data-driven mobile applications, leveraging smartphone sensor technologies such as location, camera and proximity sensors. Consuming RESTful web services will be an area of focus for the data driven components of mobile app development. You can utilize contemporary tools to build mobile applications by applying industry-standard techniques for both code-base development and user-centered design.
Module Overview
This module offers students the chance to demonstrate their ability to work independently on a significant, in-depth project requiring the coherent and critical application of computer science theory and skills.
Students must initially produce a project proposal and related materials to frame the work, specifying clear, specific, academically justified, and appropriately scoped aims and objectives, as well as feasible means for fulfilling those aims and objectives. Students then work independently to fulfil those project goals. Throughout this process students are expected to demonstrate the application of practical development and analytical skills, innovation and/or creativity, and the synthesis of information, ideas and practices to generate a coherent problem solution.
Module Overview
The field of software development is continuously evolving, driven in part by the increasing usage of generative AI and the requirement to protect applications from sophisticated cyber-attacks. This module aims to introduce students to modern development practices. Students will gain hands-on experience in tools and practices used for secure application development, deployment and monitoring.
† 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 develop a broad understanding of how data is collected, managed, analysed, and applied to real-world problems. Rather than focusing on one stage of the data process, the course is designed to help you understand the wider data landscape and develop the technical skills needed to work across it.
Data analysis and interpretation
You'll learn how to work with different types and sources of data, including databases, operational systems, web services, open data, government data, and other forms of structured and unstructured information.
You'll develop skills in:
- Sourcing and preparing data
- Data cleaning and processing
- Statistical and mathematical analysis
- Data interpretation
- Data visualisation
- Evaluating complex datasets
- Identifying patterns in real-world data
- Using evidence to support decision-making Programming and computing
You'll develop the computing knowledge needed to work effectively with data at scale. This includes areas such as:
- Programming
- Algorithms and computational methods
- Database systems
- Data storage and processing
- Data warehousing
- NoSQL and real-time data approaches
- Cloud technologies
- Software development practices
- Testing and source control
- Data and information security
You'll explore the advantages and limitations of different technologies and learn how to select appropriate tools for different data science problems.
Machine learning and artificial intelligence
You'll explore how analytical algorithms can be designed, implemented, evaluated, and optimised. Topics include:
- Predictive and advanced analytics
- Machine learning
- Artificial intelligence
- Statistical and mathematical modelling
- Simulation
- Optimisation
- Automation
- Computer vision
- Natural Language Processing
You'll consider both the capabilities of these technologies and the practical constraints involved in deploying them, including computing resources, organisational requirements, and the selection of appropriate models and tools.
Responsible data science
Working with data involves important ethical, legal, and professional responsibilities. You'll explore how data governance, security, ethics, compliance, and regulation influence data science practice. You'll consider issues including:
- Data privacy and security
- Bias and prejudice in data and analysis
- Responsible use of artificial intelligence
- Data governance
- Regulatory requirements
- Ethical decision-making
- Communicating data responsibly
This can help you understand not just what you can do with data , but how you should use it responsibly.
Learning through practical application
Teaching will combine lectures, seminars, tutorials, practical workshops, and online resources. Real-world case studies and employer-led projects will help you connect theory with practical data science challenges. You'll be assessed through a combination of coursework, presentations, project reports, posters, and in-class tests, with an emphasis on demonstrating your ability to apply your knowledge.
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
GCSE's: 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
https://www.lincoln.ac.uk/studywithus/internationalstudents/entryrequirementsandyourcountry/ for information on equivalent qualifications.
EU and Overseas 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.
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 good choice if you:
- Are interested in how organisations use data to solve problems and make decisions
- Want to develop practical skills in data analysis, programming, and computing
- Are interested in machine learning, artificial intelligence, or data engineering
- Enjoy solving problems and working with information
- Want to learn through practical projects and real-world applications
- Want to develop technical and professional skills that can transfer across industries
- Are looking for opportunities to gain industry experience through a placement year
- Want the flexibility to pursue different careers across data, technology, business, and analytics.
If you're more interested in the practical and computing side of data science, rather than a predominantly mathematical approach, BSc (Hons) Applied Data Science is designed with you in mind.
The University also offers a separate BSc (Hons) Data Science with a greater emphasis on the mathematical elements of the discipline, making the distinction between the two pathways particularly important when choosing the course that best matches your interests.
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.
What You Need to Know
We want you to have all the information you need to make an informed decision on where and what you want to study. In addition to the information provided on this course page, our What You Need to Know page offers explanations on key topics including programme validation/revalidation, additional costs, and contact hours.