Computer Science with Bioinformatics BSc (Hons)

The module will give students the opportunity to learn the fundamentals of database design. They will investigate the structure of data within a relational database, interact with, and protect the data within the database. Students will develop practical experience of problem analysis, especially concepts of data modelling, sets, relational theory and relational algebra. Students will develop database implementation skills for optimisation using structured data, held in relational databases, accessed via SQL and explore the data storage requirements of on-line businesses, companies and organisations.

This module provides the first exposure to programming in this undergraduate degree programme, and for some students their first encounter with programming at all. It introduces problem-solving and structured, procedural and function-oriented programming. Emphasis is placed on designing, developing and deploying correct, efficient, maintainable and scalable programs.

Introduction to problem solving, a brief review of Von-Neumann architecture, introduction to programming, role of compiler and linker, introduction to functions & algorithms, basic data types and variables, input/output constructs, arithmetic, comparison and logical operators, conditional statements and repetitive statements with understanding of execution flow, translation of counting, permutations and combinations into programs, introduction to modular programming, function definition and calling, stack rolling and unrolling, string and string operations, pointers/references, memory allocation and file handling.

The focus of this module is on developing knowledge, skills and understanding as they demonstrate mastery of software development. The module is based on several example programmes and emphasis is placed on practical work with the aim of ensuring that theory covered in lectures is reinforced by practical programming tasks.

This module will provide students with the opportunity to be proficient with tools and techniques used to create content accessed across a variety of mobile, tablet and desktop devices reliant on current front-end web design technologies. Students will also recognise appropriate accessibility standards and apply compliance guidelines to interactive websites. Students will develop web design skills for creating usable and interactive front-end web pages based on the principles of Human-Computer Interaction (HCI). Students will be encouraged to explore the requirements of organisations and interactive front-end development. Students will review appropriate literature on website design to gain understandings of the planning and implementation process and identify areas for further development or study.

This module teaches object-oriented programming to those who have learnt fundamental programming concepts. The main concepts discussed are: Objects, Data Abstraction, Data Encapsulation, Polymorphism, and Inheritance with practical implementation using contemporary programming language constructs. The aim is to familiarise students to the object-oriented programming paradigm, analysis and software development. The programming skills are further enhanced by teaching data structures and algorithms underpinning efficient manipulation, storage and retrieval of data. Students are exposed to the concepts of time and space complexity of computer programs.

The module introduces object-oriented design, history and advantages of the paradigm, classes, objects, data encapsulation, constructors, destructors, access modifiers, function overloading, operator overloading, association of classes: composition, aggregation and inheritance; polymorphism, abstract classes and interfaces, generic programming concepts, data and object serialization using object streams and exception handling. The module further familiarizes students with concepts of creating, storing, retrieving, ordering, and manipulation of abstract data structures and the basics of analysis of algorithms and growth of functions. Concepts of linked lists, stacks, queues, trees and graphs are discussed along with recursion and divide and conquer algorithms. Techniques of sorting (selection, insertion, merge, quick, bubble, heap and radix etc.), priority queues, sorted linked lists, binary search, traversal of trees and graphs, greedy algorithms, spanning trees, hashing and indexing are discussed and practical implementation of these using a contemporary programming language is demonstrated.

The focus of this module is on developing algorithmic thinking, object-oriented programming knowledge, skills and understanding based on medium complexity programmes. Emphasis is placed on practical work with the aim of ensuring that theory covered in lectures is reinforced by practical programming tasks.

This module provides the first exposure to programming in this undergraduate degree programme, and for some students their first encounter with programming at all. It introduces problem-solving and structured, procedural and function-oriented programming. Emphasis is placed on designing, developing and deploying correct, efficient, maintainable and scalable programs.

Introduction to problem solving, a brief review of Von-Neumann architecture, introduction to programming, role of compiler and linker, introduction to functions & algorithms, basic data types and variables, input/output constructs, arithmetic, comparison and logical operators, conditional statements and repetitive statements with understanding of execution flow, translation of counting, permutations and combinations into programs, introduction to modular programming, function definition and calling, stack rolling and unrolling, string and string operations, pointers/references, memory allocation and file handling.

The focus of this module is on developing knowledge, skills and understanding as they demonstrate mastery of software development. The module is based on several example programmes and emphasis is placed on practical work with the aim of ensuring that theory covered in lectures is reinforced by practical programming tasks.

In this module, students will develop an understanding of the ethical, professional, sustainable and legal issues relating to computer science. Students will develop critical investigative skills in applying ethical theories to novel and emerging technological outcomes. . This module will provide students with the opportunity to identify and reflect on their strengths and weaknesses and to consider requirements for their future career in computer science. Development of problem-solving skills using number theory, propositional and predicate logic will enable understanding of a range of computer science concepts. Students will participate in critical reading, writing and develop their referencing techniques with emphasis on evaluating reliability and validity of sources.

This module will introduce students to concepts involved in the fundamentals of computer networking and security. The emphasis is on the design of computer networks and fundamentals of cyber security.

In this module, students develop the knowledge, skills and understanding required to comprehend computer models, methodologies, structures, number representation, security, Boolean Algebra, logic gates and their role in basic computer systems. Knowledge of fundamentals of computer architecture is becoming increasingly important in business and finance, and are applicable to problems which have been considered mainstream computing.

An operating system is typically the lowest layer of software in a computer. It provides an abstracted interface so that applications can run on diverse hardware without modification and it provides security which prevents misbehaving software from ‘crashing’ the hardware or disturbing other tasks which may be running simultaneously. This module provides an introduction to the major principles of implementing an operating system and ensuring that these are secure.These fundamentals cover the principles and structures of network addressing, , system architecture design, transmission of media, security and operations.

This module teaches object-oriented programming to those who have learnt fundamental programming concepts. The main concepts discussed are: Objects, Data Abstraction, Data Encapsulation, Polymorphism, and Inheritance with practical implementation using contemporary programming language constructs. The aim is to familiarise students to the object-oriented programming paradigm, analysis and software development. The programming skills are further enhanced by teaching data structures and algorithms underpinning efficient manipulation, storage and retrieval of data. Students are exposed to the concepts of time and space complexity of computer programs.

The module introduces object-oriented design, history and advantages of the paradigm, classes, objects, data encapsulation, constructors, destructors, access modifiers, function overloading, operator overloading, association of classes: composition, aggregation and inheritance; polymorphism, abstract classes and interfaces, generic programming concepts, data and object serialization using object streams and exception handling. The module further familiarizes students with concepts of creating, storing, retrieving, ordering, and manipulation of abstract data structures and the basics of analysis of algorithms and growth of functions. Concepts of linked lists, stacks, queues, trees and graphs are discussed along with recursion and divide and conquer algorithms. Techniques of sorting (selection, insertion, merge, quick, bubble, heap and radix etc.), priority queues, sorted linked lists, binary search, traversal of trees and graphs, greedy algorithms, spanning trees, hashing and indexing are discussed and practical implementation of these using a contemporary programming language is demonstrated.

The focus of this module is on developing algorithmic thinking, object-oriented programming knowledge, skills and understanding based on medium complexity programmes. Emphasis is placed on practical work with the aim of ensuring that theory covered in lectures is reinforced by practical programming tasks.

This module aims to provide students the necessary theoretical and practical skills to understand, evaluate and apply statistical methods in analyses of biological data.

By completing this course, students will gain knowledge of frequency distributions and probabilities, measure of central tendencies and dispersion, elementary probability theory, laws and axioms of probability, conditional probability, application of Bayes theorem, random variables and sample spaces, binomial distribution, properties of binomial distribution, Poisson distribution, area under the curves, introduction to sampling and various sampling designs, applications of normal distributions and tests of significance, tests of independence or association, method related to one and two means, variance and covariance, heritability and its uses, analysis of variance (ANOVA), regression analysis, Pedcheck and Merlin for LOD score calculations.

Machine Learning, is a subset of Artificial Intelligence, is concerned with creating learning models that allow a computer to exhibit behaviour that would normally require a human to do. Typical applications include: computer vision, speech recognition, and intelligent robots. The learning models come in various forms, such as parametric and non-parametric and probability distributions. The emphasis is on creating these models automatically from data provided and for them to act without necessarily being explicitly programmed, for example, creating a face recognition system from a data set of facial images. In the past decade, machine learning has given society effective web search, self-driving cars, practical speech recognition, and an understanding of the human genome.

In this module, students will learn about the most effective machine learning techniques, and gain practice in implementing them successfully. Additionally, students will learn about the theoretical underpinnings of machine learning. Topics covered include: concepts of statistics & probability, supervised learning, unsupervised learning, matrix operations, backpropagation using partial derivatives, eigenvalues and eigenvectors, support vector machines and best practices in machine learning. Case studies and applications will be used to illustrate best practice in machine learning.

Advanced web development solidifies skills in website development for second-year students. The module strengthens knowledge in modern web application development using contemporary full-stack development technologies. Students will gain an understanding of applying these technologies to develop interactive and functional websites. During the module students will engage with literature and techniques for building, customising, and extending web-based development through modern implementation practices. The students will apply this knowledge by implementing techniques such as external libraries, application programming interfaces, and the interconnection between server-side and front-end development. Students will engage in industry skill building through understanding the best practices and ethical considerations of modern web development.

The Cyber Security module introduces students to the concepts required to understand and to work with computer security. These security concepts include, for example: historical background, security and security threats, practical measures (such as identification, authentication), security models, cryptography, network security). The second part of the module provides students with the opportunity to explore computer system security via hands-on activities. Current issues relating to cyber security will be discussed and possible solutions will be investigated through practical and hands-on labs Students will critically reflect, design, and develop their learning into a security-centric project to understand historical and novel cyber security threats and learn how to defend against these through practical experience.

The industry project module introduces students to the concepts, skills, and knowledge of working in a collaborative team environment in technical development projects. As part of the project, students are expected to work professionally to design, develop, and evaluate an industry-oriented project using contemporary project management principles. The module will allow students to work on an open-ended project collaboratively. Students will experience working with industry-standard approaches for managing computing projects while also building self-directed skills in their chosen specialisations. Students may seek to develop projects in their specialisms or emerging interests in Computer Science. Students will critically evaluate their work as part of the industry project and present/demonstrate the technologies to a wider audience.

This module aims to introduce the concepts of data representation, searching, security and ethical and legal constraints. Students learn and apply techniques for pattern matching, recognition and their applications in bioinformatics.

By completing this course, students will gain knowledge of data management, communication models, transmissions technology, protocols, bandwidth, topology, hardware contents, security, search engines and search process, computational methods, knowledge management, sequence and structure visualization, data mining methods and technology, pattern recognition, discovery and matching for bioinformatics systems.

The practical simulation of various bioinformatics entities, application of various bioinformatics methods are done using Python programming language with relevant libraries for applications in Bioinformatics.

The final project and critical evaluation will be structured to assess the knowledge and understanding by means appropriate to individual students, though all approaches will lead to the production of a significant piece of work that involves the demonstration of advanced practice in computer science. All approaches will be accompanied by a critical self- evaluation of the outcomes achieved. Students would integrate concepts and skills learned throughout the degree course to substantial open-ended problems. The module will increase students understanding of applied, investigational or theoretical approaches to real word problems.

This module aims to introduce the concepts of data representation, searching, security and ethical and legal constraints. Students learn and apply techniques for pattern matching, recognition and their applications in bioinformatics.

By completing this course, students will gain knowledge of data management, communication models, transmissions technology, protocols, bandwidth, topology, hardware contents, security, search engines and search process, computational methods, knowledge management, sequence and structure visualization, data mining methods and technology, pattern recognition, discovery and matching for bioinformatics systems.

The practical simulation of various bioinformatics entities, application of various bioinformatics methods are done using Python programming language with relevant libraries for applications in Bioinformatics.

Low-cost network devices add eyes, ears or sensors and arms, legs or actuators to the Internet. These devices are then connected to a computer, either physically, via wireless or through the cloud, communicating live data. This computer will then utilise machine or deep learning in the cloud. This field of study is referred to as the Internet of Things (IoT).

This module will cover the context and the history of the IoT, the hardware, communications protocols and security systems it relies on, and the cloud-side analytics that makes sense of the data produced. It will provide practical hands-on experience of common IoT devices (sensors, actuators, microcontrollers) and examine a range of commercial platforms.

Students will work with a Wi-Fi microcomputer and will program live IoT applications using that device. Students will have the opportunity to produce their own IoT device with a range of hardware and capabilities. Emphasis is placed on the versatility of components that can be integrated into such systems, as well as the diversity of data types produced by IoT edge nodes. Development of IoT applications also requires communication between different level (layers) of IoT architecture with the associated security and privacy issues.

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain.

This module will further build on the concepts of machine learning, neural networks and artificial intelligence and will introduce advanced topics in neural networks, convolutional and recurrent network structures, Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged on graphical processing units (GPUs) using contemporary architectures and frameworks. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations, and implementation of algorithms using programming languages.

In this module, students will learn about the most effective machine learning techniques, and gain practice in implementing them successfully. Additionally, students will learn about the theoretical underpinnings of machine learning. Topics covered include: supervised learning, unsupervised learning and best practices in machine learning. Case studies and applications will be used to illustrate best practice in deep learning.

This module aims to introduce decision making processing and different paradigms of computer-based decision support systems in biomedical, clinical and research settings. Use of efficient technological support systems can significantly reduce time, effort and errors in critical decision-making which is imperative in healthcare and bioinformatics systems.

The module discusses systems development using contemporary and emerging trends in machine learning and artificial intelligence, data mining pipelines, algorithms and models for big data analytics and visualization. Evaluation of developed systems, within the context of decision-support, is carried out on publicly available biomedical datasets and repositories.

The practical implementations are done using Python programming language with relevant libraries for applications in healthcare and bioinformatics.