MSci (Hons) Data Science (with Industrial Experience) Integrated Master's degree at Lancaster University

Software development is a collaborative and creative process. You will investigate the processes, tools, techniques, and notations required to successfully engage in the development of commercial grade software. Focusing on the key non-functional parameters of software reuse, scalability, maintainability and extensibility, you will explore the benefits brought by the rigour associated with object-oriented, strongly typed languages (such as Java). You will practice the concepts of composition, inheritance, polymorphism, interfaces, and traits and the commonly employed design patterns that they enable. You will also study the processes and notations associated with defining the relationships a

The creation of the microprocessor revolutionised global innovation and creativity. Without such hardware there would be no laptops, no smartphones, no tablets. Life changing technologies, from MRI scanners to the internet, would simply not exist. This module introduces the field of digital systems, the engineering principles upon which all contemporary computer systems are based. You will study the elements that work together to form the architecture of digital computers, including computer processors, memory, data storage and input/output. You will also unearth the ways in which these are enabled by digital logic, where George Boole’s theory of a binary based algebra meets electronics. Dis

Computing and data control many critical elements of modern society. It’s vital that there is a strong theoretical foundation to computer science. We begin by examining the hard questions at the centre of computer science. You will cover the fundamentals in logic, sets, and mathematics of vectors, matrices and linear algebra and their practical applications in software, such as computer graphics. Algorithms, abstract data types, and analysis of algorithms is introduced to allow you to make reasonable decisions about the design of your programs. Finally, you will get the chance to investigate the principles of data science to select, process and analyse data, and examine the way programs and

Interested in how mathematicians build theories from basic concepts to complex ideas, like eigenvalues and integration? Journey from polynomial operations to matrices and calculus through this module. Starting with polynomials and mathematical induction, you will learn fundamental proof techniques. You will explore matrices, arrays of numbers encoding simultaneous linear equations, and their geometric transformations, which are essential in linear algebra. Eigenvalues and eigenvectors, which characterise these transformations, will be introduced, highlighting their role in applications including population growth and Google's page rankings. Next, we will reintroduce you to calculus, from its

An introduction to the mathematical and computational toolsets for modelling the randomness of the world. You will learn about probability, the language used to describe random fluctuations, statistics and the mathematical techniques used to extract meaning from data. You will explore how computing tools can be used to solve challenges in scientific research, artificial intelligence, machine learning and data science. You will develop the axiomatic theory of probability, discover the theory and uses of random variables and investigate how theory matches intuitions about the real-world. You will then dive into statistical inference, learning to select appropriate probability models to describ

Software forms a central aspect of our lives. From the applications we run on our phones to satellites in space, all modern technology is enabled by software. In this module, you will focus on Software Development, the processes and skills associated with designing and constructing computer programs. Designed with your needs in mind, whether you have previous experience in computing or not, we adapt to ensure you gain the contemporary knowledge, skills and techniques needed to develop high-quality computer software. This includes a thorough treatment of the principles of computer programming and how these principles can be applied using a range of contemporary and established languages such

Human-computer interaction (HCI) is concerned with all aspects of designing, building, evaluating, and studying systems that involve human interaction. From a computing perspective, the focus is on enabling interaction through user interfaces and on creating interactive systems that provide a positive user experience. The module introduces you to the foundations of HCI, where you delve into human behaviour, technologies for interaction and human-centred design. You will review human perception, cognition and action, and relate these to design principles and guidelines. As part of this, you will discuss different paradigms of user interface and key technologies such as pointing. You will then

Statistics allows us to estimate trends and patterns in data and gives a principled way to quantify uncertainty in these estimates. The findings can lead to new insights and support decision-making in fields as diverse as cyber security, human behaviour, finance and economics, medicine, epidemiology, environmental sustainability and many more. Dive into the behaviour of multivariate random variables and asymptotic probability theory, both of which are central to statistical inference. You will then be equipped to explore one of the most fundamental statistical models, the linear regression model, and learn how to apply general statistical inference techniques to multi-parameter statistical m

Researching, collaborating, writing and presenting are key skills for all students. Collaborating with fellow students, you will investigate a chosen mathematical or statistical subject and produce a report and presentation to share your findings. As part of this, you will learn how to format and structure scientific reports and papers, use specialised documentation software like LaTeX, conduct research, cite and reference sources.

We introduce the foundational principles of systems security, focusing on Confidentiality, Integrity and Availability; and Authentication, Authorisation and Accountability (AAA). You will explore access control models, security policies and the mechanisms that underpin secure system design. You will learn about the main categories of cryptosystems (e.g. symmetric, asymmetric) highlighting their practical applications and limitations in real-world contexts. We also investigate common system vulnerabilities and the tools and techniques used by attackers. Through structured, hands-on lab sessions, you will develop practical skills in identifying, analysing and mitigating threats.

Never has the collection of data been more widespread than it is now. The extraction of information from massive, often complex and messy, datasets brings many challenges to fields such as statistics, mathematics and computing. Develop the skills and understanding to apply modern statistical and data-science tools to gain insight from contemporary data sets. By addressing challenges from a variety of applications, such as social science, public health, industry and environmental science, you will learn how to perform and present an exploratory data analysis and deploy statistical approaches to analyse data and draw conclusions. You will also develop judgement to critically evaluate the appro

Delve into the key principles of artificial intelligence (AI), touching on the core concepts and philosophy of AI and discussing its presence and ethical challenges in the modern world. Throughout, you will unearth the underlying principles of search spaces, knowledge representation and inference logic that form the core of rule-based systems, before learning the principles of machine learning, clustering, classification, linear regression and neural networks. From this, you will have the grounding necessary to progress to modules in topics such as machine learning, computer vision, and NLP. You will also gain a deeper understanding of computational problem solving, exploring the very nature

Extended reality (XR) refers to the interactive technologies that blend virtual and physical worlds into a hybrid environment or immersive experience. The technology is based on multi-modal platforms that integrate the use of widespread, wearable computing. In this module, you will explore different uses of extended reality within the reality-virtuality continuum and identify the needs and means of augmenting human senses. You will take an applied approach to the design, implementation, deployment, and evaluation of systems that are used to create an XR environment and deliver an immersive experience. To do this, you will study the latest trends in research, emerging technologies, and novel

The internet and the world wide web have now pervaded every aspect of our lives, from ecommerce and entertainment to logistics and social media. Increasingly, application software is no longer written for specific devices, but for internet web browsers. The internet has replaced operating systems as the de-facto platform for application development, making an already global phenomenon now commonplace. This module explores the various approaches to the development of internet applications, investigating both the client and server-sides, and discussing the trade-off of performance, scalability, privacy and trust associated with these approaches. You will review the role of ‘cloud infrastructur

Build upon the foundations of algorithms and their complexity to develop a deeper understanding of algorithmic approaches to computational problem solving. Explore computational complexity theory, which allows us to consider the very nature of computability - including non-deterministic polynomial (NP) complexity classes such as NP-hard, NP-complete and the classes of problems which cannot be solved. You will be introduced to classical approaches to problem solving such as divide and conquer, recursion, and parallel approaches, emphasising their relative benefits and weaknesses to different classes of problem. You will also study advanced data structures in depth, such as tries, heaps, suffi

We explore a practical and applied aspect of cyber security: penetration testing. You will learn common approaches and tools that attackers use to undermine the security of digital systems and gain first-hand experience of the weaknesses that can be present in real-world systems through guided work in highly controlled, small-group practical labs. The module will wrap the technical and theoretical aspects within the llegal, regulatory and ethical frameworks for the appropriate application of ethical penetration testing.

This module provides a practical and theoretical background to the design, implementation, and use of database management systems, both for data designers and application developers. It incorporates consideration of information quality and security in the design, development, and use of database systems. You will be introduced to a brief history of database management systems, Entity-Relationship Models, the relational model and the data normalisation process, and alternative schema definitions, NoSQL and object-oriented data models, big data, as well as transaction processing and concurrency control. The module embeds practical access and retrieval considerations and how to interact with da

Computing plays a pivotal role in addressing growing energy costs, greenhouse emissions and the climate crisis. Whilst we can use computing and its associated digital technologies to shape a greener society (as well as create more energy-efficient software and hardware), there exist important trade-offs with respect to economic cost, engineering effort and environmental impact. You will explore key concepts associated with creating sustainable computing, spanning from how a processor uses electricity to how computers shape a greener economy and society. You will study the methods to create more energy-efficient code, energy-aware device mechanisms, as well as the benefits and drawbacks of co

You will undertake a substantial individual project, typically involving the principled design, implementation, and evaluation of a substantial piece of software, experimental study, or theoretical work. To assist in this, an academic will provide a large range of project ideas from both the School of Computing and Communications and the School of Mathematical Sciences, which you will rank by level of interest before being allocated to a supervisor. You will also have the opportunity to write your own project idea and find a supervisor that would like to support you, and projects can be carried out in collaboration with an external partner, such as a company. Throughout the project, you will

Dive into alternative programming language paradigms, beyond imperative and object-oriented programming. Emphasis is placed on functional programming languages and their unique constraints and features, such as more expressive type systems, immutability, pure functions and side-effects, lambdas, higher order functions, currying, map/reduce and pattern matching. You will also explore why functional languages bring about increased reliability and scalability and how they are now experiencing a resurgence within the software industry. Through hands-on laboratory sessions, you will learn a functional programming language, such as Haskell, and see how functional programming concepts are being int

Understanding how data evolves over time is crucial across numerous sectors, from finance and engineering to climate science. Develop the tools to analyse temporal data, detect structural changes and build predictive models. Using changepoint detection algorithms, you will learn how to identify abrupt changes in the mean or variance of a process, or parameters in a regression model. These methods will be introduced from a foundational perspective, developing both computational and mathematical understanding. You will then learn to handle temporal dependence by studying a popular range of time-series models, using these to generate insights about the data and produce forecasts. Throughout the

Learn how to teach computer science as a discipline, including organising engaging activities that address the digital skills gap, and inspiring new computer scientists. Through practical sessions, you will build a foundational understanding of computing pedagogy, learning to recognise how learners study computer science and arrange teaching to respond to their needs. You’ll explore the instruments and methods for effective teaching practices, considering UK and global contexts, and the differences within primary, secondary, and higher education. The importance of equality, diversity, and inclusion (EDI), ethics, safeguarding and integrity considerations in education will be highlighted thro

Computer vision is a branch of artificial intelligence which aims to build computer-based systems that can interpret and draw meaning from digital images. This module digs into the fundamentals of image formation, information relating to the human visual system, and image interpretation methodologies including convolution, edge detection and feature extraction, and comparison. You will tackle key problems in current research, including semantic segmentation, object detection and three-dimensional image interpretation. You will cover a range of approaches, from low-level image processing to convolutional neural networks. At the end of the module, you will be equipped to construct software com

Digital Health explores the utilisation of digital technologies in healthcare. These technologies have an ever-growing role to play in transforming health and care delivery and supporting individuals to improve their health. Discover the practical applications, implications, and how to enable technologies of digital health. You will survey sensor technologies that permit remote and automated patient monitoring and study the technologies and processes that enable patient-driven healthcare. You will also investigate the structure of health data in electronic health records and methods for the evaluation of digital health solutions. Alongside these applied topics, you’ll also learn about data g

Statistical techniques are often applied to environmental data, such as air temperatures, rainfall or wildfire locations. You will learn about some of the common features of such datasets and how these features are used to design statistical models. You will first be introduced to the Gaussian process model for continuous spatial processes. You will learn about the properties of the Gaussian process and implement this model for spatial data analysis, before investigating methods for point-reference data, such as earthquake or wildfire locations. You will also dip into natural hazard risk management, which seeks to mitigate the effects of events, such as flooding or storms, in a manner that i

All programming languages are based on theoretical principles of formal language theory. In this module, you dive deep into formal languages representation and grammars, and how they relate to programming language compilers and interpreters. You will study formal language syntax and semantics, phrase structure grammars, and the Chomsky hierarchy. You will learn how to classify languages and explore the concepts of ambiguity in context-free grammar and its implications. In particular, you will learn about the compilation process including lexical analysis and syntactic analysis, recursive descent parsers and semantic analysis. Finally, you get to investigate the synthesis phase, where interme

Delve into machine learning, a fundamental concept in artificial intelligence that enables a computer to learn how to perform a task from data rather than traditional programming. In this module, you will study the key ideas and techniques behind machine learning and develop the practical skills needed to understand the implications and potential of machine learning in business and society. You will begin by looking at real-world problems, challenges, and current machine learning methodology. Building on this, you will cover a variety of approaches to machine learning, from decision trees to a wide range of deep neural networks, including multilayer perceptrons, convolutional neural networks

Statistical methods play a crucial role in health research. This module introduces you to the key study designs used in health investigations, such as randomised controlled trials and various types of observational study. Issues of study design will be covered from both a practical and theoretical perspective, aiming to identify the most efficient design which adheres to ethical principles and can be carried out in a feasible amount of time, or using a feasible number of patients. Various approaches to controlling for confounding will be discussed, including both design and analysis-based methods. You will also explore different types of response data including time-to-event data and the res

Gain a broad understanding of Natural Language Processing (NLP), a branch of artificial intelligence where computational methods are used to analyse and understand human languages. Throughout the module, you will be exposed to the core concepts surrounding the NLP pipeline, covering methods and techniques for data collection, cleaning, tokenisation, and annotation using a hierarchy of linguistic levels (e.g. morphology, syntax, and semantics). You will experiment with and comparatively evaluate different methods and techniques, including rule-based, probabilistic, machine learning and deep learning approaches. You will also learn to apply and adapt NLP pipelines and tools to real-world text

We introduce you to quantum computing's core principles and applications, contrasting its capabilities with classical systems. You will master Dirac notation and essential linear algebra, before examining quantum mechanics' four postulates, including qubits, gates, and circuit models. You will cover fundamental algorithms, including Deutsch's algorithm (implemented via Qiskit), Simon's problem, Bernstein-Vazirani, Grover's search (with BBBV Theorem analysis), and Shor's factorisation algorithm's impact on RSA cryptography. Quantum cryptography components address post-quantum security and QKD protocols, while quantum information theory explores superdense coding, the no-cloning theorem and te

Artificial Intelligence (AI) is being rapidly adopted in both research and industry, via technologies such as generative AI and large language models (LLM). They are being used for a range of applications by enhancing cyber security through the detection of anomalies, identifying threats, and monitoring abnormal activities. However, AI itself is susceptible to various attacks, such as prompt injection, data leakages, jailbreaking, bypassing guardrails, model backdoors, and more. In this module, you will learn the fundamentals of AI for security and security for AI. This encompasses both how AI can be leveraged to augment and improve established cyber security techniques (from firewalls, risk

Understand security threats to cyber physical systems (CPS), such as industrial control systems, Internet of Things and connected vehicles, as well as techniques to mitigate these threats. Compared to traditional computer systems, CPS have limited resources and are typically deployed into a physical environment. This impacts the implementation of security techniques, as due to the environment they are deployed in you must consider both digital and physical attacks. This module introduces how to identify the appropriate security techniques to use for a CPS. You will come to understand how to write secure applications for CPS and which alternative mitigations are appropriate. You will also lea

Distributed systems are the foundation upon which modern large-scale infrastructures are built, such as Cloud and service-oriented architectures (also known as ‘as a service’). You’ll investigate the cryptographic techniques used to build such systems, and secure distributed systems themselves. You’ll study the design approaches to constructing a secure distributed system, including the common vulnerabilities and attack surfaces associated with distributed systems, and the widely adopted design patterns used to mitigate them. To ensure the correctness of such systems, you will be introduced to formal verification techniques covering system specification and the verification of their correctn

Building on the statistical techniques explored so far, you deepen your understanding of both the theoretical underpinnings and practical application of frequentist statistical inference. You will then be introduced to an alternative paradigm: Bayesian statistics. The frequentist perspective views all probabilities in terms of the proportions of outcomes over repeated experimentation and has been the foundation of hypothesis testing and experimental design over years of data-driven science and research. Meanwhile, the increasingly popular Bayesian approach arises directly from Bayes theorem, avoiding hypothetical repeated sampling. As a result, Bayesian statistics is often more intuitive and

Statistics and machine learning share the goal of extracting patterns or trends from very large and complex datasets. These patterns are used to forecast or predict future behaviour or interpolate missing information. Learn about the similarities and differences between statistical inference and machine learning algorithms for supervised learning and how the two approaches can be used for classification and prediction. You will explore the class of generalised linear models, which is one of the most frequently used classes of supervised learning model. You will learn how to implement these models, how to interpret their output and how to check whether the model is an accurate representation

Stochastic processes are fundamental to probability theory and statistics and appear in many places in both theory and practice. For example, they are used in finance to model stock prices and interest rates, in biology to model population dynamics and the spread of disease, and in physics to describe the motion of particles. During this module, you will focus on the most basic stochastic processes and how they can be analysed, starting with the simple random walk. Based on a model of how a gambler's fortune changes over time, it questions whether there are betting strategies that gamblers can use to guarantee a win. We will focus on Markov processes, which are natural generalisations of the