Data Science with Year of Professional Experience

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Start date: September 2024

Deadlines for on-time applications

2024 entry application deadlines

For courses starting in 2024 (and for deferred applications), your application should be with us at UCAS by one of these dates – depending on what courses you apply for. If your completed application – including all your personal details and your academic reference – is submitted by the deadline, it is guaranteed to be considered.

16 October 2023 for 2024 entry at 18:00 (UK time) – any course at the universities of Oxford and Cambridge, or for most courses in medicine, veterinary medicine/science, and dentistry. You can add choices with a different deadline later, but don’t forget you can only have five choices in total.

31 January 2024 for 2024 entry at 18:00 (UK time) – for the majority of courses.

Some course providers require additional admissions tests to be taken alongside the UCAS application, and these may have a deadline. Find out more about these tests at https://www.ucas.com/undergraduate/applying-university/admissions-tests

Check course information in the search tool to see which deadline applies to you at the application weblink below.

Apply as soon as possible: Student funding arrangements mean that as offers are made and places fill up, some courses may only have vacancies for students from certain locations. It’s therefore really important that you apply for your chosen courses by the appropriate deadlines mentioned above, as not all courses will have places for all students.

All applications received after 30 June are entered into Clearing - find out more about Clearing at https://www.ucas.com/undergraduate/clearing-and-results-day/what-clearing

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.

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.

The information below is intended as an example only, featuring module details for the current year of study (2023/24). Modules are reviewed on an annual basis and may be subject to future changes – revised details will be published through Programme Specifications ahead of each academic year.

Year 1
Core Modules
• Introduction to Algebra and Analysis (30 credits)
• Databases (20 credits)
• Introduction to Probability & Statistics (30 credits)

Optional Modules
• Object Oriented Programming (20 credits)
• Computer Science Challenges (20 credits)
• Procedural Programming (20 credits)

Year 2
Core Modules
• Linear Algebra (20 credits)
• Data Structures Algorithms PL (30 credits)
• Professional and Transferrable Skills (20 credits)
• Statistical Inference (20 credits)
• Methods of Operational Research (20 credits)
• Introduction to Artificial Intelligence and Machine Learning (20 credits)

Year 3
Core Modules
• Year of Professional Experience (120 credits)

Year 4
Core Modules
• Video Analytics& Machine Learn (20 credits)

Optional Modules
• Concurrent Programming (20 credits)
• Deep Learning (20 credits)
• Malware Analysis (20 credits)

Details of assessments associated with this courses are outlined below:

The way in which you are assessed will vary according to the Learning objectives of each module. Some modules are assessed solely through project work or written assignments. Others are assessed through a combination of coursework and end of semester examinations. Details of how each module is assessed are shown in the Student Handbook which is provided to all students during their first year induction.

Admissions
Tel: 028 9097 3838
Fax: 028 9097 5151
Email address: admissions@qub.ac.uk