Computer Science and Artificial Intelligence BSc (Hons)

30 credits

This module is a Level 5 core module for the BSc in Computer Science and Artificial Intelligence. It builds upon the skills students will have acquired in the Level 4 modules, primarily in mathematics, statistics and programming, and will provide them with a solid foundation in A.I. in preparation both for their Individual Project and other modules at Level 6, or for an appropriate Industrial Placement (if taken).

This module will introduce students to the principal paradigms of Artificial Intelligence, and their relation to other disciplines, such as Cognitive Science, and their applications to practical problems. These will include both traditional (logic and search based) and more modern (Machine Learning) approaches (including neural networks), and will also cover some real-world practical case studies.

It will cover the "core" mathematical, statistical and machine learning concepts that are essential attributes for employable data scientists and machine learning engineers in the modern, data-driven world, and include an introduction to the importance of ethical debate in both the development and application of A.I., with contemporary case studies, and issues relating to data privacy and data protection.

15 credits

This module will establish the skills required to effectively use databases for storage and retrieval of information. You will learn how to design, build and query databases according using logical data models and Structured Query Language (SQL).

The module will develop the theoretical and practical knowledge to design, develop and manage data models using modern database management systems.

On successful completion of the module, you will be able to:

• Design a conceptual data model that captures an organisation's data requirements for a database.
• Convert conceptual models to physical models (e.g. relational data model) and apply normalisation.
• Build efficient and effective databases using SQL and write queries to retrieve data.
• Articulate the role of data administration, security and recovery processes in database management systems.

15 credits

This module builds upon the skills acquired in Level 4 and will deepen students' knowledge and understanding of mathematical concepts that underpin aspects of Computer Science and Artificial Intelligence.

The module can be considered to have two components.

The first develops the mathematical skills required to understand the principles of probabilistic and statistical methods in the context of data analysis. This is followed by an introduction to descriptive statistics and probability distributions, through to applications of applied statistical techniques which are fundamental to the exploration, data insight, and modelling techniques, which are in turn, essential in making sense of datasets, regardless of size.

The second component covers some of the more practical aspects of data analysis and visualisation in practice and will be taught with a highly hands-on, data driven, application led approach.

Students will be introduced to the practicalities of using real life data covering processes for collecting, manipulating and cleaning data and of quality assurance of data sources. This module will provide students with basic data modelling and interpretation skills that feeds into data-driven decision-making. The module focuses on applicable and practical skills commercially and contextually relevant software and techniques.

30 credits

This module is designed to encourage you to look outwards to industry and employability, and make you more confident when facing the job market upon graduation. You will develop excellent group working skills, broaden your understanding of industry and its requirements, and enhance your project management skills.

It is always exciting to watch the groups collaborating as they produce their entrepreneurial projects for Kingston University's "Bright Ideas" scheme; students involved in this have won awards, producing fascinating artefacts for their remaining term and exploring new and different areas within their course field.

The module is an opportunity to work with many different students, working in subject areas that are innovative and unusual. Very often, a student may discover something they did not know that they could achieve. This triggers new learning and an added confidence that was not previously there. Those are the times that the learning is strongest, and our pride in the student, the greatest.

30 credits

This module takes what you did in Programming 1, where you started to think like a programmer, and focuses on one programming language to develop software applications that people can interact with.

You will continue to use Kingston's bespoke NoobLab in the first term – no other university offers this platform or unique approach to learning – to go into more depth in creating models of "real world" objects, as well as data structures like arrays, lists and maps, and reading/writing files. In the second term you will build simple applications (graphical user interfaces). 

You will use Java Swing as the basis for a graphic user interface, drawing on the Java Collections Framework as needed to build data structures for your applications. We will introduce industry-standard development environments such as NetBeans and Visual Studio Code, alongside GitLab for source and version control of your code.

30 credits

This module takes you on a journey, from underlying computer architecture through to operating systems and networking and finally on to cloud computing. We designed this module to enhance your understanding of how modern computer systems are designed and used.

You will explore the essential features of each of these technologies and acquire both theoretical and practical knowledge. In addition, you will also develop knowledge of containerisation, parallel programming and algorithm complexity to make use of new parallel and cloud computing architectures to design application scenarios for today's modern computing environments.

30 credits

This module will equip students with knowledge and experience in the concepts and methods in machine learning and artificial intelligence needed to evaluate their applicability to real-world problems. Students will apply machine learning and artificial intelligence algorithms and techniques in appropriately-scaled real-world applications. They will also gain a thorough awareness and understanding of the importance of ethical issues in A.I. and its applications, and can carry out an assessment of the ethical implications of particular developments and applications of A.I.

On successful completion of the module, students will be able to:

• Demonstrate understanding of and skills in applying advanced A.I. methodologies
• Discuss the issues in problem domains such as Computer Vision, Natural Language Processing and Anomaly Detection which make them unsuitable for solution using traditional methods alone, and how A.I. can address these.
• Identify, apply and implement appropriate A.I. approaches for solving problems from those domains studied
• Critically evaluate and discuss to what extent use of such approaches have been successful, including their legal, ethical, societal implications, and suggest alternative solutions when appropriate

30 credits

This module will consolidate and build on previously acquired knowledge of databases by analysing and evaluating important issues in the database area. In addition, advanced aspects of data warehousing and data mining will be studied, encompassing the principles and commercial application of the technologies.

30 credits

The goal of the module is to further develop skills in organisation, timekeeping, research literature, developing and critically analysing results as well as reporting work verbally and in a written format. The end result will be an artefact or artefacts which demonstrate creativity and technical competence as well as a technical report.

On successful completion of the module, students will be able to:

• Carry out a literature search to summarise and evaluate background work relevant to the chosen course, undertake an investigation of the planned topic and critically evaluate the outcomes.
• Plan tasks within time and other commitment constraints.
• Produce a well-structured written report demonstrating a sound understanding of the theory of the chosen project area including relevant references and use of language, diagrams, tables and graphs where necessary.
• Present or answer questions clearly and concisely in a structured interview about their work.
• Create an artefact relating to the chosen course.
• Identify and take account of relevant legal, social and ethical issues.

30 credits

This module will expand your skill set and your awareness as software industry professional, using industry-relevant tools, know-how, as well as theoretical areas. Our focus is on real-world experience, and preparing you to become a skilled graduate, regardless of the job roles you are considering or the skills you want to expand on within the computing domain.

This will empower you to become better at whichever role you are aiming at; developer, analyst, designer, project manager and so on. This is not a code-centric module; the skills in this module are more concerned with efficiently working within a software development team and managing the code produced rather than necessarily writing code yourself.

You will explore the latest practices for making more dependable, reusable and scalable software systems, and how to configure, maintain and support such systems. We will also discuss topics on software architecture, and how to select the right architecture based on the system requirements. We will also cover popular industry practices like refactoring, software measurements, DevOps, Microservices, and software testing approaches, and even a bit of Quantum computing.

The workshop activities promote collaborative learning. Within the first teaching block you will explore a specific topic or tool every two weeks. In the second teaching block, you will work as a team, taking up a role as a manager, developer, analyst, designer, security expert or tester and work on a software development case study.

Most of the topics and tools you come across in the module are linked to skills advertised as essential or desirable by software industry employers. They are also closely linked to interview questions or assessment centre activities. Overall, the main focus of this module is to equip you to become a skilled and knowledgeable IT professional capable of facing challenges, while maintaining a holistic, practical perspective on the issues at hand.

30 credits

This is an optional module intended for undergraduate students who are studying Computing-related subjects. Although it forms part of the User Experience guided pathway it can be taken as a standalone module and previous experience of UX is not assumed. This module will focus upon the skills, methods and tools required in careers such as UX Architect, UX Designer, Service Designer, Information Architect or Digital Product Designer. The curriculum is finely balanced between theory and practice. Students are directly immersed in organisational practices and skills used in industry and will make use of academic theory in this practical context. Students will learn to develop investigative, analytical, technical, communication and advocacy skills to help them shape interactive technologies that augment people's abilities, enhance their creativity, connect them to others and protect their interests. They will also become aware of the impact of levels of digital literacy, availability of and access to technology, economic and business drivers, regulations, and regional/cultural norms. The module will also develop methods and skills required to understand current users, to investigate non-use, and to imagine future users.

30 credits

While this module provides a foundation for careers in mobile application development, mobile is becoming increasingly ubiquitous and the skills taught also have applications in UX, web development and software engineering in general. Although there are no prerequisites, it is assumed that students have acquired a general familiarity with programming and software development principles through their previous study.

The module is divided into two phases. In the first phase, students will be introduced to software development for the two major mobile platforms. This will cover development environments for these platforms, UI conventions, building and deploying simple applications. Students will then be introduced to cross platform development environments for mobile development.

In the second phase, standard frameworks for mobile web development will be introduced. The phase is organised around a practical project. Students will choose one of the platforms on which to build a mobile application of their choice. This project gives students the opportunity to specialise and explore their chosen platform in greater depth, acquiring the knowledge and proficiency to be able to design and build complex mobile apps. Students will be encouraged to publish their apps in one or both (in the case of a cross-platform app) of the two major app stores, thus providing an introduction to mobile application delivery and distribution.

30 credits

Programming is a central activity of software development, which encompasses a wide range of languages, environments and specialisms.

This module will offer students the opportunity to acquire a useful competence across this range. The first teaching block will cover aspects of language, algorithms, tools, test-driven methodologies, and a range of user interface technologies.

The second teaching block will apply these themes across a range of technologies and application environments, focusing on web and mobile in particular. Assessment will include computer-based in-class tests and e-portfolios of student achievement that allow them to curate and share their passion for programming.

15 credits

This Level 6 option module is oriented towards Data Science, a significant and essential subfield of modern Computer Science and Artificial Intelligence. Data science has many application areas ranging from medicine to climate science and business analytics.

This module builds on several topics covered in earlier parts of the programme including mathematical and statistical modelling, probability estimation and visualisation. Students will be introduced to the key statistical tests for frequentist data science, where specific theories are explored and tested via descriptive and inferential statistics. They will apply their existing programming knowledge to execute these tests in an appropriate programming language using existing statistical libraries.

Students will learn to write computer programs that can read, process and analyse textual and numerical data, for the purposes of data insight and interpretation. This module will provide students with a data science skillset to include critical thinking, communication, teamwork and leadership skills that feed into data-driven decision-making. The module focuses on practical applications to hone commercially and contextually relevant knowledge and competences.

15 credits

The module initially begins by using and building upon the distribution theory met in Levels 4 and 5. Students are introduced to the important aspects of statistical estimation including the Neyman-Pearson's lemma with its useful and interesting applications. We then embark on the Bayesian methodology and its applications to the statistical decision-making problems including loss and risk functions. In particular, we make use of both Bayesian actuarial credibility theory and the evaluation of the credibility coefficient. Furthermore, practical applications of Bayesian methodology are illustrated and compared with classical approach. The advantages involved in decision-making are identified and discussed.

15 credits

This module comprises the application of time series modelling techniques in forecasting with background gained in earlier modules. We cover both non-probabilistic algorithmic methods and the probabilistic Box-Jenkins ARIMA modelling techniques. The methods are applied to real and up-to-date time series data sets using MS Excel and SAS Enterprise software packages. Emphasis is placed on practicability of methods and illustration of their real-world applications. Students will have the opportunity to acquire, develop and consolidate both modelling and software skills through a series of exercises during practicals and tutorial sessions.