Data Science with Foundation Year

This module supports and broadens your learning through practical experiments and application of mathematical knowledge. As an engineer or mathematician you’ll need to develop a variety of experimental and transferable skills as part of your education and ongoing professional development.  

You’ll study topics such as: 

• Mechanical laboratory experiments 

• Electrical and electronic practical work and skills 

• Mathematical case studies with real-world problems 

• Town planning and traffic management  

• Monitoring natural phenomena 

• How to design experiments 

• How to deal with experimental errors 

• Scientific writing 

This module introduces you to core topics in science, physics and mathematics. You’ll develop an awareness of mathematics contexts and your ability to apply mathematics appropriately. 

You’ll study topics such as:  

• Numbers and order of operations 

• Algebra, mathematical functions and solving simple equations 

• Geometry including trigonometry of right-angled triangles 

• Vector addition and resolution 

• Technology for calculations and plot graphs  

• Ohm's Law and capacitance 

• LR, CR and LCR circuits  

• AC theory  

• SI units and motion in a straight line (rectilinear) 

• Forces, friction and equilibrium 

This module consolidates your previous learning, developing further essential skills in science, physics and mathematics. You’ll study mathematical, electrical and mechanical areas of the subject with a structured approach to problem solving.   

You’ll study topics such as:   

• Functions and product, quotient, chain rules, gradients, min/max problems 

• Integration by function, parts, substitution and integrals 

• Velocity and acceleration problems 

• Trigonometric graphs, equations and sine/cosine rules 

• Logarithmic and exponential functions and graphs 

• Inverse and simultaneous equations using matrices 

• Electronics for measurement, diodes, power supplies and sensors 

• Op amps, analogue to digital converters and microcontrollers  

• Circular motion, work, energy, power and simple harmonic motion 

• Specific and latent heat, changes of state, expansion/contraction 

This module studies the physical and chemical principles underpinning materials science, introducing the structure, processing and characteristics of engineering materials. You’ll explore different manufacturing techniques, associated equipment and tooling, while developing your presentation and information skills.  

You’ll study topics such as: 

• Atomic and electronic structure of atoms 

• Chemical reactivity and electronic structure 

• Ionic bonding, crystals, compounds and covalent bonding  

• Characteristics and examples of organic compounds 

• Metallic bonding and conservation of mass in chemical reactions 

• Metals, organic polymers, ceramics and composites  

• Health, safety and solidification processes  

• Hot and cold metal working processes 

• Ceramic and polymer processing 

• Machining processes 

This module develops your knowledge and understanding of key statistical techniques, statistical software and project-based skills, working  

collaboratively with local partners on an applied project. You’ll learn through a mix of lectures, workshops and laboratory sessions. 

You’ll study topics such as: 

• Project-based statistics, from data collection to conclusions  

• Statistical programming  

• Data manipulation, summary and visualisation 

• Commonly used statistical techniques and statistical modelling 

• Working with employers as a data analyst  

• Consultancy and project management skills 

This module introduces computer programming and its deployment in a mainstream programming language for data science and artificial intelligence applications. You'll learn fundamental programming concepts and develop essential skills for the entire data processing pipeline — from collecting and cleaning raw data to performing sophisticated data analysis. You'll gain essential skills in statistical concepts and simple machine learning models, along with hands-on experience in creating AI-driven applications. The module uses popular programming languages with straightforward examples to help you build a solid foundation for further learning in data science. 

You'll study topics such as: 

• Computational thinking and problem-solving strategies for data analysis tasks 

• Data Processing system development lifecycle - from data collection to model deployment 

• Data types, structures, and manipulation techniques for large datasets 

• Algorithms, control structures, and basic machine learning concepts 

• Object-oriented programming for AI applications 

• Code quality and best practices in data science 

• Development environments and tools for data science and AI 

• Generative AI technologies with ethical AI concept for maintaining academic integrity 

Enhance your Data Science project portfolios and employability through hands-on projects and professional curriculum vitae (CV) writing workshops. 

This module develops the skills required of a professional mathematician, including reading, thinking and working with mathematical and statistical ideas, and programming. In lectures, workshops and group tutorials, you’ll develop confidence and accuracy with foundational concepts and techniques, extending these to new topics which are essential for successful further study.   

 You’ll study topics such as: 

• Functions, solving equations and complex numbers 

• Calculus, analytical and numerical techniques for basic differential equations 

• Matrices and linear algebra 

• Core concepts of statistics  

• Probability and inferential statistics  

• Programming  

This module explores problem solving and mathematical thinking skills by introducing mathematical theory and abstraction. Through interactive workshops you’ll develop an appreciation of rigour and methods of mathematical proof. 

You’ll study topics such as: 

• Problem-solving strategies 

• Communicating solution methods with clear definitions and justifications 

• Connecting and applying prior learning and skills to new problems 

• Proof methods 

• Set theory and logic 

• Number bases 

• Modular arithmetic 

• Functions 

• Group and graph theories 

• Combinatorics 

Aims: 

This module will impart key concepts in computer science to students and to further develop important skills in computer-based problem-solving and data manipulation.  

Indicative Content  

• Introduction to algorithms (role and importance of algorithms in computer science). Concurrent, and parallel algorithms 
• Sorting algorithms (merge sort, heap sort, quick sort) 
• Searching algorithms 
• Linked lists (Singly linked lists; double linked lists) 
• Stacks and Queues 
• Recursion  
• Tree structures (creating, searching, traversing, merging). 
• Graph structures (traversals, activity networks, critical paths, shortest paths) 
• The theory of algorithms (Big-O notation, Computability; Turing machines). 
• Complexity (computational and control flow). 
• Optimisation problems (Travelling Salesman Problem, Bin Packing). 
• Optimisation search methods (Genetic algorithm, Hill Climbing, Simulated Annealing, Tabu search algorithm, Iterated Local Search).  

This module delivers knowledge and practical skills of machine learning algorithms for artificial intelligence applications. It will present the key mathematical principles and concepts required to create machine learning algorithms through data-driven approaches. You will gain practical experience while designing, implementing, and evaluating machine learning systems to solve real-world artificial intelligence problems. 

You’ll study topics such as: 

• Data-driven approaches to machine learning 

• Linear regression and gradient descent  

• Regression and classification problems  

• Machine learning algorithms: decision trees, support vector machines, ensemble learning and k-mean/mean-shift clustering 

• Data pre-processing, cleansing and feature extraction  

• Evaluating machine learning system by using cross-validation, confusion matrix and receiver operating characteristic 

This module will introduce a comprehensive approach of data organization within a database, encompassing the methods of storage, retrieval, access, and manipulation in a cloud-based environment. 

INDICATIVE CONTENT 

This module will acquaint learners with the principles of storing, retrieving, and analysing data through the use of databases. Learners will have the opportunity to investigate the mechanisms by which data is stored and transferred across systems within a cloud environment. The curriculum will cover various types of data, their respective storage structures, and methodologies for data extraction, including techniques for transferring data between systems utilizing APIs and connectors, specifically through the potential use of for example, SAP SAC. 

You’ll study topics such as: 

• Database operations: How to perform database operations, such as development, performance optimization, and migration  

• Data pipelines: How to build and manage data pipelines using graphical interfaces  

• Data processing: How to process data in real-time, in batches, or as streams  

• Data storage: How to store data in cloud-based storage systems  

• Data security: How to protect data from breaches  

• Data availability: How to ensure that data is available in a usable format- Data quality, data cleaning 

• Brief discussion about GDPR and be able to manage data in an ethical way for maintaining academic integrity. 

• SFIA (Skills Framework for Information Age) concepts to enhance understanding of different pathways in the field of IT. 

This module develops your theoretical understanding and practical application of more advanced statistical techniques that are used widely in industry. You’ll also expand on techniques covered at level 4. 

You’ll study topics such as:  

• Statistical modelling and application 

• Model formulation and selection  

• Identification of issues 

• Non-linear models  

• Understanding the relevant theory 

• Probability and theory of statistical inference 

• Bayesian statistics 

Module aim:    

The aim of this module is to enhance students’ professional development through the completion of and reflection on meaningful work placement(s).  

A work placement will provide students with opportunities to experience the realities of professional employment and experience how their course can be applied within their chosen industry setting. 

The placement will: 

• Allow student to apply the skills, theories and behaviours relevant and in addition to their course 
• Enable students to enhance their interpersonal skills in demand by graduate employers – communication, problem-solving, creativity, resilience, team work etc
• Grow their student network and relationship building skills
• Provide student with insights into the industry and sector in which their placement occurs 
• Help student make informed graduate careers choices

Indicative Content:  

In this module students undertake a sandwich placement (min 24 weeks / min 21 hours per week) which is integrated, assessed and aligned to their studies.  

Their personal Placement Academic Supervisor (PAS) will be their key point of contact during their placement and will encourage and support students to reflect on their experience, learning and contribution to the organisation they work for.  

To demonstrate gains in professional development, students will be required to share their progress, learning and achievements with their Placement Academic Supervisor and reflect on these for the summative piece of work. 

Machine Learning techniques, such as artificial neural networks, are widely used in modern intelligent systems. The module aims to deliver key concepts, theories and practical skills to implement state-of-the-art neural networks techniques within the artificial intelligence problems domain. This module enhances the knowledge and experiences from corresponding level 5 machine learning modules and focuses on smart system design, development, evaluation and optimisation.

Indicative Content

• Artificial and Deep Neural Networks

• Algorithms of training Neural Networks

• Customising pre-trained models and transfer learning

• Training large scale neural networks through GPUs, multiple devices and cloud computing

• Computer vision tasks with convolutional neural networks

• Natural language processing with recurrent neural networks

• Reinforcement learning algorithms

• Generative learning algorithms

This module is a research project of your choice – you’ll identify a computer-based problem, investigate the requirements, analyse results of research undertaken and design, and develop and evaluate a solution to that problem. You’ll then evaluate the project’s success, your learnings and opportunities for further work. 

You’ll apply skills and learning such as:

• Ideation and planning a larger-scale project 
• Information gathering and literature reviews 
• The selection of tools, techniques or methods 
• Implementation, testing and user evaluation 
• Critical reflection on project deliverables, success or failure 
• Referencing and citation techniques 
• Legal, social, and ethical considerations  
• Security and confidentiality 
• Sustainable development and deployment 
• Employability skills and attributes 

This module emphasizes the necessity of incorporating ethical design principles into AI and data science projects, highlighting the importance of fairness, transparency, and accountability. Learners will explore data ethics, including privacy, consent, and bias, ensuring responsible data handling throughout its lifecycle. The module also covers data security, focusing on protecting data from unauthorized access and breaches, and maintaining integrity and confidentiality.  

Students will engage with topics of data protection, and fairness in AI by developing collaborative research, to identify the risks and develop strategies for mitigation. Students will be challenged to discuss issues surrounding bias and inequality in context of data science to be able to thrive as reflective practitioners. 

INDICATIVE CONTENT 

The module will cover relevant regulations, governance frameworks, and standards essential for ethically aligned design, highlighting their connection to various stages of the data science lifecycle. Students will engage with real case scenarios to apply this knowledge and be challenged in applying these techniques and tools in areas including data anonymization, debiasing, and fairness testing.  

By integrating these principles learners will be able to thrive and collaborate in the design and implementation of innovative, ethically sound, and secure AI and data science projects, building trust and credibility in AI systems. 

You’ll study topics such as:  

• Data Protection and Privacy Regulations  

• Technologies for anonymizing data: k-anonymity, and differential privacy. 

• Risk and Accountability Regulations  

• Professional Ethics and Codes of Conduct  

• Ethical practices of data handling including issues of inequality, bias, and fairness in domain 

• Conflicts of Interest and Ethical Decision Making Client Relationships, Communication, and Complaints Handling.  

• Strategies to reduce race and gender discrimination from automated systems 

This module enables students to explore cutting-edge programming techniques for artificial intelligence. Students will engage with complex real-world data science and AI challenges and will develop innovative solutions through hands-on experience. The curriculum encompasses comprehensive coverage of data processing systems, from advanced collection methods to production-level deployment, whilst emphasising professional coding development practices. In addition, the module also incorporates professional development opportunities, including portfolio building and career preparation workshops, ensuring graduates are well-prepared for industry demands.  

You'll study topics such as: 

• Advanced programming techniques for data manipulation and analysis 

• Machine learning algorithms implementation and optimisation 

• Deep learning frameworks and neural network architectures 

• Big data processing and distributed computing 

• Data visualisation and interactive dashboard creation 

• API development and integration for AI services 

• Cloud AI platforms and edge computing for AI applications 

• Model deployment and scalability considerations 

• Explore Generative AI applications with large language/visual models while maintaining academic standards through AI-aware assignment design and documentation requirements 

• Ethics and responsible AI development  

• Professional development and industry readiness workshops.