AI and Data Science

This module introduces computer programming in a high level programming language, such as Java and includes principles and practice in problem solving, program design, solution implementation and testing.

This module introduces you to the use of the relational model to structure data for efficient storage and retrieval. You will gain practical experience in the construction and usage of relational databases in an industry-standard relational database management system.

This module introduces a range of skills required for you to succeed both on your degree and into graduate employment. The module aims to help you to develop your own personal, independent, and proactive studying methodology to be effective life-long learners.

This module provides the essential mathematical foundation for further study in computing, covering a variety of applied mathematical topics suitable for a range of computing disciplines.

This module offers you the opportunity to collaborate on a programme-specific team project, collecting ideas from across their first year of study. Working in a team offers you the chance to develop both independent and team working skills, project planning, and helps you prepare for the second year of study.

This module provides an introduction to the key processes of data science from an applied perspective. You will gain the required skills for operating fluently within a data science environment.

This module covers the principal mathematical and statistical topics required to pursue further study in the area of data science.

Building on the knowledge and skills that you have developed so far on the programme, the thematic project will allow you to further develop your project management, team working and communication skills by applying those skills to a given industry, or research, inspired project. Wider aspects such as social, security, ethical and legal issues will be embedded in the project work where appropriate.

This module explores a range of fundamental algorithms and data structures used to solve a number of computational challenges fundamental to the study of computer science disciplines. You'll learn to consider and discuss algorithm complexity and efficiency through practical implementation.

This module continues to explore the storage and retrieval of data, furthering your appreciation for the selection, usage and evaluation of different models and architectures of database for different tasks.

This module introduces you to the notion of predictive modelling in machine learning from a practical, problem-solving perspective. You will gain an understanding of the fundamental topics of predictive tasks and will explore contemporary techniques for predictive modelling and model evaluation.

This module capitalises on the uniquely vibrant technology community in Manchester, challenging you to curate a portfolio of professional interests within the field by engaging with the local tech community at various events. Through self-reflection, you will develop new interests, and learn to see your course in a wider technological, social and ethical context.

This module covers the theoretical, and practical, foundations of machine learning from a problem-solving perspective. A range of machine learning topics are explored which will enable you to conduct machine learning experiments on given data sets.

This module continues to develop student's skills and knowledge in the area of machine learning by exploring the core aspects and techniques of deep learning. These skills and knowledge provide students with the required background for operating fluently within a deep learning ecosystem.

The synoptic project allows you to consolidate your learning from your degree programme into a final project, bringing together ideas and techniques from throughout your study. From this project, you will produce the centrepiece of their degree work portfolio.

This module explores the theoretical and practical aspects of scalable, efficient problem-solving and data processing for computationally intensive and big data challenges. You will be introduced to industry-relevant tools and techniques for solving large-scale problems.

This module will prepare you for the synoptic project by introducing the scientific method and presenting the main approaches to perform, analyse, classify and communicate research.

This module explores aspects and tasks relating to the life-cycle of data in organisations and the wider issues relating to data handling. You will explore the societal impact of data and models, and contemporary issues relating to the legal and ethical challenges of data storage and processing.

This module offers you the opportunity to explore the development of applications designed to run on mobile computing devices. You will gain practical experience of using device emulators, frameworks, APIs and libraries for the development of mobile apps that utilise specialist features of mobile devices.

The Research in Computing option cluster offers you the opportunity to engage with the Department of Computing and Mathematics’ Centre for Advanced Computational Science, by choosing an option which relates to one of the centre’s main themes. By undertaking a focused research study in a specialist area, you have the opportunity to engage in cutting-edge computational research informed by world-leading theoretical and applied research.

Stressing the importance of a user-centred design approach from the outset, this module affords you the chance to widen your knowledge of the ways in which humans interact with digital systems and services. It draws upon theories and principles from human-computer interaction, user experience, and interaction design, exploring how these can be applied and evaluated in a variety of technological contexts with the overarching aim of promoting an improved and impactful solution.

This module challenges you to develop your problem-solving skills under pressure. By participating in a series of rapid, applied problem solving challenges, you will develop core essential skills aimed at bridging the employment gap. You will become well-versed in analysing problems, identifying appropriate algorithms and data structures, creatively solving those problems, implementing and evaluating solutions. You will enhance your employability by practising assessment tasks commonly used in tech industry recruitment.