Data Science with Year of Professional Experience (BSc)

The aim of the programme is to offer a deep and up-to-date education in data science, machine learning and artificial intelligence that prepares graduates with key knowledge, skills and competencies necessary for employment in data engineering, data analysis, data architect (as well as managerial positions on those topics), or as preparation for further research and innovation careers. 

In particular the programme aims to provide students with:

• Comprehensive knowledge and understanding of the fundamental principles of artificial intelligence, data science and machine learning, which will remain applicable through changes in technology.
• Advanced knowledge and practical skills in the theory and practice of data analytics.
• The necessary skills, tools and techniques needed to embark on careers as data scientist, or professional developers skilled in data science.
• Skills in a range of practices, processes, tools and methods applicable to data science in commercial and research contexts.
• Timely exposure to, and practical experience in, a range of current technologies and emerging trends at the forefront of data science, such as Deep Learning, Natural Language Processing and Trustworthy AI.
• Opportunities for the development of practical skills in a commercial context.

Data Science with Year of Professional Experience highlights
World Class Facilities

• A new Teaching Centre for Mathematics and Physics opened in September 2016. This provides a dedicated space for teaching within the School. Facilities for mathematics include new lecture and group-study rooms, a new student social area and state-of-the-art computer facilities. Computer Science teaching takes place in the Computer Science Building on the Malone Road, just a short walk from the Mathematics department. The building was recently refurbished at a cost of £14M, and is welcoming with a modern style and approach to students with spaces which include computer laboratories, lecture theatre and options of break-out areas.

Industry Links

• Our students are constantly given the opportunity to put theory into practice. We regularly consult a large number of employers including, for example, Civica and Sensata Technologies, who provide sponsorship for our students as well as Kainos and Liberty IT who are members of the employer liaison panel for the course.
• In addition, students will complete a year of professional experience on data analytics with one of our partners companies.

Course Structure
Introduction

• Mathematics is the universal language of science while computer science is the study of the hardware and algorithms that are used in modern computer systems. Since many of the early pioneers of computer science, for instance Alan Turing, were mathematicians it is not surprising that these two subjects are closely related. This is a three-year joint degree programme between the School of Electrical and Electronic Engineering and Computer Science and the School of Maths and Physics, that combines the study of the two subjects so a holistic approach to Data Science and Machine Learning, from theory to practice, can be provided.

Stage 1
Themes may include:

• Object Oriented programming
• Databases
• Procedural Programming
• Computer Science Challenges
• Web Technologies
• Software Design Principles
• Introduction to Algebra and Analysis
• Introduction to Probability and Statistics

Stage 2
Themes may include:

• Professional Practice
• Transferable Skills for the IT Sector
• Introduction to Artificial Intelligence and Machine Learning
• Statistical Inference
• Linear Algebra
• Introduction to Enterprise Computing
• Data Structures, Algorithms and Programming Languages

Stage 3

• Year of Professional Experience on Data Analysis

Stage 4
Themes may include:

• Deep learning
• Video Analytics and Machine Learning
• Concurrent Programming
• Cloud Computing
• Malware Analysis
• Fairness, Interpretability and privacy in machine learning
• Data Science Project
• Discrete Mathematics
• Stochastic Processes and Risk
• Bayesian Statistics
• Financial Mathematics
• Mathematical Methods of Quantum Information processing
• Linear models
• Information Theory

In addition to some of the above modules, students are expected to complete:
Final year project on data Analysis

Learning and Teaching

• The School has a world class reputation for research and provides excellent facilities, including access to major new research centres in Secure Information Technologies, Electronics, Communications and Information Technology and Sonic Arts. A number of modules on the course are closely linked to the research expertise of these centres and evolve and change rapidly to reflect some of the current, emerging and exciting developments in the field.
• At Queen’s, we aim to deliver a high quality learning environment that embeds intellectual curiosity, innovation and best practice in learning, teaching and student support to enable student to achieve their full academic potential.

Interactive Lectures

• Technologies such as Turningpoint allow interactive and active lectures, where lecturers can pool students anonymously and in real time from the slides.

Year 1
Core Modules

• Procedural Programming (20 credits)
• Data Driven Systems (20 credits)
• Introduction to Probability & Statistics (30 credits)
• Introduction to Algebra and Analysis (30 credits)
• Object Oriented Programming (20 credits)

Year 2
Core Modules

• Data Structures and Algorithms (20 credits)
• Introduction to Artificial Intelligence and Machine Learning (20 credits)
• Linear Algebra (20 credits)
• Professional and Transferrable Skills (20 credits)
• Statistical Inference (20 credits)
• Theory of Computation (20 credits)

Year 3
Core Modules

• Year of Professional Experience (120 credits)

Year 4
Optional Modules

• Deep Learning (20 credits)
• Malware Analysis (20 credits)
• Cloud Computing (20 credits)
• Concurrent Programming (20 credits)

Career Prospects
Professional Opportunities

• Student completing this degree are expected to move to a professional or a research position in data analytics and machine learning, with application to different sectors: Fintech, Health and Biomedical, Security, Agriculture, etc.

Degree Plus/Future Ready Award for extra-curricular skills

• In addition to your degree programme, at Queen's you can have the opportunity to gain wider life, academic and employability skills. For example, placements, voluntary work, clubs, societies, sports and lots more. So not only do you graduate with a degree recognised from a world leading university, you'll have practical national and international experience plus a wider exposure to life overall. We call this Degree Plus/Future Ready Award. It's what makes studying at Queen's University Belfast special.