About this course
Want to change direction? Switch careers? Upskill? This fast-track Master’s is for you.
As a conversion course, this MSc is suitable for students with a range of backgrounds in STEM and non-STEM subjects.
Don’t have programming experience? That’s okay. You’ll learn Python coding at the start of the course to make sure you’re up to speed.
Unlike other universities, you’ll study the full breadth of AI – not just one specialism. You’ll cover programming, statistics, machine learning, big data, data visualisation, computer vision and the ethical and legal responsibilities of using data.
You can design your own research project to suit your background and career interests. Throughout the programme students will work on a case studies from one of our industry partners such as Naimuri, the NHS, KCOM or Lampada Digital Solutions.
You’ll develop key skills including programming, problem-solving, and data visualisation and interpretation. And graduate at the forefront of data science.
Choose your modules
Unlike other universities, you’ll cover the full breadth of AI – not just one specialism. From programming and machine learning, to big data and ethical responsibilities. In trimester one you’ll take an AI and a data science module, together with the programming module. In trimester two, you’ll advance your AI and data science skills with further modules.
All modules are subject to availability and this list may change at any time.
Programming for AI and Data Science
Learn the fundamentals of Python coding so you can progress onto the rest of the course.
Assessment: Portfolio of work
Type: Core
Credits: 20 credits
Understanding Artificial Intelligence
An introduction to the fundamental concepts in Artificial Intelligence, and their application. Topics include:
- Origins of AI: What is AI? From early history to the Dartmouth conference and the present day; Intelligent agents, and performance measures
- Learning, Frameworks and Packages: Introduction to supervised learning; Regression; Classification; Clustering; Artificial Neural Networks; Convolutional Neural Networks; Keras; Tensorflow
- Implications for Society: Legalities; Ethics and professional implications; Social consequences
This module is assessed by a portfolio of work, in the form of a programmed code and a corresponding technical report.
Type: Core
Credits: 20 credits
Fundamentals of Data Science
An introduction to the principles of data science and data analysis. Topics include:
- Data Science Context: Datafication of society and the history of data science.
- Properties and types of data (e.g., quantitative and categorical data)
- Classification and regression, introduction to Kaggle and other sources of data
- Data Management: Data collection and techniques; Cleaning of data and processing; Data errors and artefacts; missing data
- Introductory statistical approaches to data: Basic mathematical concepts; Introduction to probabilities; Descriptive statistics (e.g., centrality measures) and characterizing distributions; Correlations; Statistical hypothesis testing
- Data analysis and visualization: Types of visualization and interpretation; Identifying outliers; Regression models
- Applications: Real-world data applications, including examples
This module is assessed by a presentation and project report.
Type: Core
Credits: 20 credits
Big Data and Data Mining
The module will build on the concepts introduced in the first data science module and introduce Big Data and Data Mining, including network analysis. Topics will include:
- Databases, including the use of the SQL language.
- Association Pattern Data Mining: the Brute force approaches and A priori algorithm.
- Sorting Algorithms: Bubble sort
- Clustering: DBSCAN
- Time series analysis: ARIMA: XGBOOST
- Web Scraping/spidering: Beautiful Soup; Legal and ethical aspects
- Network Analysis: social media, graph theory, network visualisation and similarity measures
This module is assessed by a presentation and a project report.
Type: Core
Credits: 20 credits
Applied Artificial Intelligence
The module will build on the concepts introduced in the first AI module, and prepare you for your dissertation. Topics include classification revisited, deep learning, applications to problems, cognitive bias, and implications for equality.
Assessment: Presentation and project report
Type: Core
Credits: 20 credits
Research and Application in Artificial Intelligence and Data Science
The module contains two themes that are strongly interrelated to each other:
The first theme offers options to study how AI and Data Science apply to real-world contexts. Options could include sustainability, healthcare, social responsibility, the creative industries, and the natural environment.
Alongside the first theme, you’ll develop your own research proposal to tackle a genuine research project. You’ll draw from the experiences in the options to identify questions and limitations associated with your proposed research. This will prepare you for your dissertation in Trimester 3.
Type: Core
Credits: 20 credits
Artificial Intelligence and Data Science Research Project
Plan and work independently on your own complex research-based problem. And report on the aims, methods and outcomes of your scientific investigation.
Type: Core
Credits: 20 credits
Dissertation
This Dissertation gives you the chance to tailor your research project according to your interests gained throughout the taught element of your MSc programme. The dissertation is the pinnacle of the MSc course and allows you to build upon your knowledge gained in the previous taught modules, by carrying out research based on real-world business challenges that will prepare you for the complexities of entering the job market.
