Course units
All Course Units are structured along three learning groups: coding (orange), critical and computing theory (blue) and project based (green), with the project-based units leading up to the final data science project.
Year 1
Coding 1: Introducing Computer & Data Science (20 credits)
This unit will introduce you to the discipline of computer science and the sub-discipline of data science and their effects on the wider world that we all live in. Each is a mixture of art, science, and engineering that continues to shape us, from individuals to large organisations like governments. To better understand their influence, you will become literate in computer programming languages such as JavaScript and Python and become familiar with basic mathematics that are useful for applications from basic data processing to real-time multimedia programming
Mathematics and Statistics for Data Science (20 credits)
This unit will introduce you to the fundamentals of mathematics and statistics. You will explore key theories and approaches that support contemporary statistical reasoning, and the general mathematical principles upon which they depend.
Data, Representation and Visualisation (20 credits)
In this unit, you will explore how information is represented as data, and how different types of data can be organised, stored, analysed and interrogated. You will also learn how to use different programming languages and data representations to create, navigate and analyse complex data structures.
Coding Two: Fundamentals of Digital Systems (20 credits)
This unit will further expand your knowledge, skills and competencies in programming and computer architecture by looking at how our world is made computable by processes of digitisation and the application of digital logic. First, you will learn how computing hardware uses binary representation of numbers and binary operations to make decisions on a small scale. Then, you will explore how computing processes scale up using matrix mathematics to make sense of and manipulate images and other large data sets. A combination of programming languages will be used to take advantage of their abilities to work with data at different scales.
Database Systems for Data Science: Hybrid and Cloud Integration (20 credits)
This unit will introduce you to a range of mathematical approaches required for carrying out modern data science including calculus, discrete structures, probability theory, elementary statistics and fundamental linear algebra/matrix maths. This unit will introduce fundamentals of data, managing database systems, handle relational and NoSQL databases, entity modelling and ER diagrams and also discuss case studies under data science applications of databases. This unit will also introduce to few cloud-based data storage and management which will be later utilised towards your project developments in later years.
Data Governance and Privacy Compliance and computational Ethics (20 credits)
In this unit, you will be taught what it means to represent people as data points and explore the effects of data abstraction at a macro scale on individuals and marginalised groups. You will also explicitly look at the use of data in public policy making. Also this unit will intend to cover the aspects of the data regulation conditions in the acquisition, processing, and storage of data in computer systems. Furthermore, investigate legal definitions encompassing Personal Data, Intellectual Property, and Data ownership within the context of key concepts of UK legislation governing data collection, storage, and access (beyond data governance, Bias etc)
Year 2
Coding Three: Algorithms and Complexity (20 credits)
You will be introduced to a range of standard algorithms used in computing for topics such as searching, sorting, data structures, and analysis. You will implement them and analyse their efficiency, performance, and complexity using formal and informal types of notation that will be introduced in the unit. You will also think critically about how the abstract, automated decision-making of algorithms might have unintended or negative effects on real-world data, especially how they might have harmful effects on smaller populations due to a primary focus on efficiency.
Machine Learning for Data Science and Ethical Computing (20 credits)
In this unit, you will learn the fundamental principles of Machine Learning crucial for Data Science and AI. You will be able to achieve expertise in the advanced data wrangling skills in the realms of data cleaning and pre-processing, building upon the knowledge acquired in earlier modules. This unit will also help you to explore the application of various Machine Learning algorithms within the domain of data science, while also gaining insights into the pivotal role of ethical considerations with fairness as a critical principle to eliminate bias and ensure equal treatments
Data Science Project: Software Development with Database Integration (20 credits)
In this unit, you will explore the dynamic intersection of data science and software engineering. Commencing with principles of project initiation, define objectives and align software engineering practices with the distinctive requirements of data-driven projects. Uncover data engineering foundations to grasp effective strategies for data collection, pre-processing and including the integration of a database into your project.
Coding 4: Deep Learning and Big Data Integration: Technologies and Tools (20 credits)
In this unit you will explore the convergence of deep learning and big data in this unit, examining processing intricacies through tools like MapReduce. You will gain insights into distributed storage, retrieval principles, and construct efficient data pipelines. Throughout, you will cover topics such as Hadoop, Spark, data warehousing, and diverse storage technologies, mastering the integration of deep learning for advanced big data analytics.
Organisations and Computing Entrepreneurship (20 credits)
This unit allows you space to develop your understanding of what type of organisations exist that you may want to work for, with, or to start on your own. You will explore a range of different organisations in different industries, such as companies, start-ups, non-profits, divisions of governments, NGOs and small collectives through case studies and guest lectures. These will help you understand why different organisations have different structures and business models and may lead you to third-year placements, since this unit appears early in year 2 of your studies.
Data Science Project: Software Development with Big Data and Cloud
In this unit, you will deliver a substantial software project based on knowledge and competencies that you have developed so far on the course.
Year 3
Coding Five: Artificial Intelligence and Advanced Analytics (20 credits)
Machine learning and Artificial Intelligence is at the core of modern industries. The ML competencies that you have developed so far will come handy and this unit will examine more complex intelligent systems design, cloud-based deployments including neural networks, reinforcement learning and other critical techniques such as Natural Language Processing and Computer Vision and apply them to different contexts such as audio, video and image generation and data analysis.
Data and Cybersecurity (20 credits)
This unit will build on your understanding of contemporary data security methods. You will be taught to use techniques including static program analysis and threat analysis. You will also use tools to analyse security risks in online applications.
Business Analytics Project: Strategic Product Development (20 credits)
During this unit, you will learn advanced approaches to product development including project management skills, time cost analysis estimation, product architecture and testing procedures from the business perspective
Responsible Data Science: Equity, Consent, Innovation and Ethics (20 credits)
In this unit, you will consider and reflect on critical approaches to technology development, particularly as they pertain to data science and AI, building on the design ethics work delivered throughout the course so far. You will be encouraged to apply these techniques to your Final Year Project, exploring how you have applied your knowledge of computing ethics in your work.
Data Science and AI: Final project (20 credits)
This will be your final thesis project, where you will demonstrate your skills and understanding of a range of creative computing methods and approaches including statistical methods, software engineering, data visualisation, machine learning and AI, NLP, CV, data security, and other essential topics in the discipline.
Diploma in Professional Studies (Optional year)
The Diploma in Professional Studies (DPS) is an optional placement year in industry between the second and third year of the course. It is a managed year of professional experience, largely undertaken in the design profession in a variety of national and international locations. Successful candidates are selected on a competitive basis from academic performance and studentship, successful completion of the Diploma Higher Education (year 2) and by portfolio and proposal.
Refer B.Sc Computer Science handbook for the proposed unit details (Shared course)
