About the course
Data is one of the 21st century’s most valuable commodities. Understanding how to analyse, validate and interpret it to inform decision making are key skills in just about every walk of life. Nationally, there is a widely recognised shortage of qualified Artificial Intelligence (AI) and data scientists to meet the needs of industry.
This course is will equip you with the skills and professional insight you need to launch a career in this fast-growing sector. The programme is aimed at STEM (science, technology, engineering and mathematics) and non-STEM graduates, who want to develop their digital skills. It’s also suitable for people who are looking to upskill and improve their career prospects.
You will learn Python coding, so experience in programming is not required.
You’ll cover topics such as programming, statistics, machine learning, big data, data visualisation, computer vision and the ethical and legal responsibilities of using data. Learning is delivered online and on-campus through a series of bespoke taught modules. In the third semester, you will do an academic dissertation or an industrial placement, where you will apply your knowledge to real-world problems, using data science and AI solutions. The University has teamed up with a range of employers to offer internship opportunities to students.
At the end of the course, graduates will have developed key competencies in AI and data science, including programming, data visualisation, problem-solving and data interpretation.
You will be able to apply AI and data science techniques to real-world problems; critically evaluate AI and data science methodologies; plan, design and carry out empirical research, and interpret, present and communicate the outcomes of data science and AI solutions.
The course combines expertise from Departments across the Faculty of Science and Engineering, including Computer Science, Physics and Mathematics. You’ll have access to VIPER – one of the most powerful high-performance computers in the Higher Education sector. Our Computer Science research is ranked 5th in the UK for impact.
To support widening participation students undertaking full-time study in 2021/22, the University has 15 scholarships available, worth £10,000 each. Applications for these bursaries as open, with a deadline for applications of 2 July 2021 at 23:59 UK time. We aim to make decisions on the applications for bursaries by the end of July 2021. The scholarships will be prioritised for women, black and disabled students and are being funded by the Office for Students (OfS), Department for Digital, Culture, Media and Sport (DCMS), Department for Business, Energy and Industrial Strategy (BEIS) and the Office for Artificial Intelligence (OAI). Find out more.
What you'll study
Key areas of study for the programme will include concepts and methodologies related to AI, data science and data analytics. The programme will begin with an intensive module in programming skills where you will learn to code from scratch, to ensure everyone is prepared to undertake the data science and AI modules that follow.
- Core programming skills and techniques, including designing and coding applications, and the important principles of code design and development.
- Data science tools and techniques, including the principles of data science, data analysis, visualisation and interpretation, and the use of “big data”.
- Artificial intelligence tools and techniques, including problem-solving, knowledge representation, machine learning, computer vision, human-computer interactions and (mis) information diffusion.
- Ethical computing and data science, exploring the ethical, legal, social and professional frameworks in which data scientists must operate, in business and society.
- The application of AI and data science in research and industry
You will take one AI and one data science module in trimester 1, together with the programming module.
In trimester 2, further AI and data science modules will allow you to develop your skills and knowledge and prepare for your dissertation by exploring case studies and developing a research proposal.
The dissertation project will be based either with one of our industry partners, solving real-world data science or artificial intelligence problems, or in a disciplinary area relevant to your background and/or career goals.
The University is working with a range of regional partners, including Humber Outreach Partnership; Spencer Group; J.R Rix & Sons; KCOM; The Deep; Lampada Digital Solutions; Optalysys and C4DI to offer internships with real-world business projects.
How you’ll learn
Delivery of the core content will take place online. Face-to-face sessions will focus on problem-solving, group work and the application of core knowledge, and allow the use of the University’s computer science facilities.
On-campus teaching will be blocked into a limited number of days per week (in 2020, this was every Friday), and fixed throughout the course. You will be encouraged to bring your professional and learning experiences to your degree, and to work with students of differing experiences to maximise the benefits of the programme.
There will be online support to help with the transition to postgraduate study, incorporating topics such as returning to study, expectations, the language of postgraduate study, and time management.
Learning, teaching and assessment will be characterised by diverse assessment types and an avoidance of recall-based timed examinations. You will be allocated a tutor to support your academic development.
All modules are subject to availability and this list may change at any time.
Programming for AI and Data Science
The module content is designed to ensure that you are equipped with the fundamental programming skills in Python to allow you to successfully undertake the remainder of the programme.
Techniques that will be taught include:
• Installation and Packages, including specific packages for machine learning, data science, and artificial intelligence.
• Fundamentals of coding
• Documentation, File Handling and Lists, Variables and Data Types, Functions and Loops, Conditions and Data Manipulation, Graphing and basic visualization, Debugging and testing.
This module is assessed by a portfolio of work. You'll attend one workshop per week (maximum of six hours).
Fundamentals of Data Science
An introduction to the principles of data science and data analysis. Topics include:
• Data Science Context: Datfication 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: 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; Regression models
• Applications: Real-world data applications, including examples
• The legal framework and ethical implications of data science
This module is assessed by a presentation and project report. Over six weeks, you'll attend one synchronous workshop (maximum of six hours) per fortnight.
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 and unsupervised learning; Convolution neural networks, Capsule neural networks; Keras; Tensorflow
• Implications for Society: Legalities; Ethics and professional implications; Social consequences
This module is assessed by a portfolio of work. Over six weeks, you'll attend one synchronous workshop (maximum of six hours) per fortnight.
Big Data and Data Mining
This module builds on the concepts introduced in the first data science module and provides an introduction to Big Data and Data Mining, including distance measures. Topics will include:
• Databases, including use of the SQL language.
• Association Pattern Data Mining: the frequentist approach; Apriori algorithm.
• Clustering: filtering; k-means and associated approaches.
• Outliers: definitions of outliers; Extreme values and their detection
• Data Classification and Vizualization: Filtering, Trees, and Rules; example algorithms and using visualizations in context.
This module is assessed by a presentation and a project report. Over ten weeks, you'll attend one synchronous workshop (maximum of three hours) per fortnight.
Applied Artificial Intelligence
The module will build on the concepts introduced in the first Artificial Intelligence module and provide you with the skills and knowledge to undertake your dissertation. Topics will include:
• Classification: revisiting the classification problem; Hyperplanes; Naive Bayes classifiers
• Deep Learning: neural networks; Autoencoders and Deep Learning
• Applications to problems: real-world problems, and solving them with AI, Natural language processing
• Legal, societal, ethical and professional implications of using AI, including cognitive bias in AI solutions and the implications for equality.
This module is assessed by a presentation and a project report. Over ten weeks, one afternoon per fortnight will be synchronous workshops (maximum of 3 hours).
Research and Application in AI and Data Science
This module links the practical AI and data science techniques developed through the remaining taught modules, to their use and application in the real world. This module contains two themes that are strongly interrelated to each other.
• Theme 1: Case Studies.
In this theme, you will examine a set of case studies from real world examples of the applications of both data science and artificial intelligence. The cases will be drawn from both academia and industry with attention paid not only to the spread of cases themselves, but also the diversity of the people that conducted the studies. These case studies will be used to illustrate how data science and artificial intelligence are shaping the world.
• Theme 2: Research Proposal Development.
In tandem with the first theme, you will develop a research proposal to tackle a genuine research project. This will prepare you for the MSc research project and dissertation in the third trimester. You will draw from the experiences in the case studies to identify questions and limitations associated with their proposed research.
This module is assessed by a report on the case studies, and a project proposal. Over ten weeks, one morning per fortnight will be synchronous workshops (maximum of 2 hours).
Artificial Intelligence and Data Science Research Project
This module is the capstone of the MSc, drawing together the skills and knowledge built up through the programme and allowing you to demonstrate your competence in using AI and/or data science techniques.
This module aims to develop your ability to:
• Plan and work independently on a complex research-based problem in artificial intelligence and/or data science.
• Report on the aims, methods and outcomes of a scientific investigation.
Each research project will be particular to the individual concerned. The actual work carried out will depend on the topic but may include experimental planning and organisation of equipment and/or services (e.g., VIPER), computer programming in artificial intelligence and/or data science, data acquisition and analysis, study of related literature and critical evaluation of the outputs.