Computing - with Major Options

About this course
The strong practical focus of the programme culminates in a project practicum, carried out over the summer months. Typically, students will develop a prototype software system in their Major area that targets a real-world problem. They may also analyse processes or techniques, and propose and evaluate alternatives. Most projects are individual but, exceptionally, may be carried out as part of a team.

Students may also be sponsored by external clients or develop their own ideas. Typically, projects commence with a feasibility study, followed by the creation of a project plan and development of a software application or rigorous theoretical analysis.

Over the duration of the programme, students will develop employment-enhancing skills across a number of key areas. These include:

Enhancement of proven ability to engineer software
Improvement of knowledge of operating systems and networks
Development of strong, team-based skills, developed through significant project work during the course
Enhanced communication skills through scheduled presentations to lecturers and peers
Improved understanding of the business and social context of their work and awareness of new directions
Development of research skills to enable contribution of novel ideas, methods and tools to new challenges in their professional careers.

MSc in Computing (with Major Options)
Major 1 - Natural Language Processing (This Major is available Full Time Only)
Natural Language Processing (NLP) combines computer science, linguistics and artificial intelligence. The aim of NLP is to develop computer programs with the ability to understand and produce text, as demonstrated in recent chatbots like ChatGPT, LaMDA, and BARD. NLP is an exciting field, because it has the potential to transform the way we interact with machines and each other, and to make our lives easier and more efficient. In cutting-edge research, large language models like the one at the heart of ChatGPT are even beginning to be used for increasingly general AI system development.

Natural Language Processing specialists are in high demand. They are needed to develop and improve technologies such as text classifiers, chatbots, virtual assistants, and language translation systems, which are used by many industries, including e-commerce, healthcare, finance, and more. With the increasing amount of data being generated, the ability to process and understand human language has become crucial for many businesses to make informed decisions. As a result, there is a shortage of skilled NLP professionals, and companies pay highly competitive salaries to attract talent in this field.

This is the first Natural Language Processing master’s degree in Ireland. It has been developed by world-leading academics working in NLP, and is taught by a team of experts from a diverse range of computer science backgrounds including natural language processing, data science, artificial intelligence, and machine learning. An expert panel of industry NLP professionals will deliver a series of guest lectures and engage in practicum supervision. Taught intensively over nine months and building on your background in computer science, this master’s will equip you with the skills you need for a successful career in this fast-growing field.

Major 2 - Data Analytics (This Major is available both Full Time and Part Time)
This exciting new Major, delivered in conjunction with leading industry players, builds on the School of Computing's expertise and its involvement with Insight, Science Foundation Ireland's Centre for Data Analytics and ADAPT, the centre for new Human Centric AI techniques. Technologies such as the internet, sensor nets, social media and cloud computing are generating vast amounts of data. To say we are drowning in information is an understatement. Yet in this vast amount of raw data, there are gems of knowledge that can be used to improve processes and generate value. This Major provides students with a deep understanding of the issues, techniques and tools to examine large amounts of raw data in order to extract meaningful conclusions from the information these contain.

Major 3 - Artificial Intelligence (This Major is available Full Time Only)
There is a strong demand for graduates with the highly specialised multi-disciplinary skills that are required in AI, both as practitioners in the development of AI applications and as researchers into the advanced capabilities required for the creation of next-generation AI systems. This Major is designed to meet this educational need, by providing a balanced programme of instruction across a range of relevant areas.

Major 4 - Secure Software Engineering (This Major is available both Full Time and Part Time)
In this modern age of increased data usage and ubiquitous computing the security of software is more important than ever. This updated and revised MSc. Major in Secure Software Engineering builds a firm base of advanced software engineering skills and emphasises security from start to finish. It will be appropriate for all those tasked with building and researching secure software systems.

Major 5 - FinTech & Technology Innovation (This Major is available Part Time only)
The innovation enhanced by the emergence of Financial Technologies (FinTech) holds the prospect of a shift of power over everyday financial transactions away from those who have hitherto held it (in large Financial Organizations) and towards the general population, leading to a potential ‘democratisation’ of finance in areas such as Aggregation, Micro Investing and Crowd-funding. Other key application areas of FinTech Innovation have been towards empowering companies in the Financial Services sector, predominantly in Payment Services and Regulatory Compliance by simplifying and automating their processes.

In this major we draw a distinction between those who actually develop the products which have the potential to empower and those who would use them in a business context. It has been developed to deliver the requisite FinTech background knowledge in key underpinning areas such as Data Governance and Financial Time Series as well as technologies necessary in developing Innovative FinTech technologies e.g. AI and Blockchain.

Programme Academic Structure for 2023 - 2024, M.Sc. in Computing
This information is provisional & subject to change.

Full-time Programme Structure
Year 1 Optional Modules -
Code Title Credit Semester Exam % CA % Resit Category
CA271B Introduction to Machine Learning 7.5 Semester 1 0 100 1
CA4015 Advanced Machine Learning 7.5 Semester 2 0 100 1
CA4023 Natural Language Technologies 7.5 Semester 1 60 40 1
CA6005 Mechanics of Search 7.5 Semester 2 0 100 1
CA6010 Foundations of Natural Language Processing 7.5 Year long 60 40 1
CA6011 Deep Learning for Natural Language Processing 7.5 Semester 2 30 70 1
CA6012 Human Factors in NLP 7.5 Semester 1 0 100 1
CA6014 Practicum (Natural Languages Processing) 30 Autumn Semester 0 100 2
CA6015 Advanced Machine Learning 7.5 Semester 2 0 100 1
CA6071 Introduction to Machine Learning 7.5 Semester 1 0 100 1
CA640 Professional & Research Practice 7.5 Semester 1 50 50 1
CA642 Cryptography & Number Theory 7.5 Semester 1 75 25 1
CA645 Network Security 7.5 Semester 2 70 30 1
CA647 Secure Programming 7.5 Semester 1 70 30 1
CA648 Formal Programming 7.5 Semester 2 75 25 1
CA650 Software Process Quality 7.5 Semester 2 75 25 1
CA652 Artificial Intelligence, Info & Info Seeking 7.5 Semester 2 50 50 1
CA659 Mathematical Methods/Computational Science 7.5 Semester 2 100 0 1
CA660 Statistical Data Analysis 7.5 Semester 1 75 25 3
CA670 Concurrent Programming 7.5 Semester 2 75 25 1
CA675 Cloud Technologies 7.5 Semester 1 0 100 1
CA681I Machine Translation 7.5 Semester 2 0 100 1
CA682 Data Management and Visualisation 7.5 Semester 1 75 25 3
CA683 Data Analytics and Data Mining 7.5 Semester 2 75 25 1
CA684 Machine Learning 7.5 Semester 2 75 25 1
CA685 Data Analytics Practicum 30 Autumn Semester 0 100 2
CA686 Foundations of Artificial Intelligence 7.5 Semester 1 60 40 1
CA688 Blockchain: Basics and Applications 7.5 Semester 1 40 60 1
CA689 Practicum (Artificial Intelligence) 30 Autumn Semester 0 100 2
CA694 Practicum (SSE Practicum) 30 Autumn Semester 0 100 2

Part-time Programme Structure
Year 1 Optional Modules,
Code Title Credit Semester Exam % CA % Resit Category
CA640I Professional & Research Practice 7.5 Semester 1 50 50 1
CA642 Cryptography & Number Theory 7.5 Semester 1 75 25 1
CA642I Cryptography and Number Theory 7.5 Semester 1 75 25 1
CA645 Network Security 7.5 Semester 2 70 30 1
CA650 Software Process Quality 7.5 Semester 2 75 25 1
CA652 Artificial Intelligence, Info & Info Seeking 7.5 Semester 2 50 50 1
CA660 Statistical Data Analysis 7.5 Semester 1 75 25 3
CA675 Cloud Technologies 7.5 Semester 1 0 100 1
CA683 Data Analytics and Data Mining 7.5 Semester 2 75 25 1
CA683I Data Analytics and Data Mining 7.5 Semester 2 75 25 1
CA684I Machine Learning 7.5 Semester 2 75 25 1
CA686I Foundations of Artificial Intelligence 7.5 Semester 1 60 40 1
CA687I Cloud Systems 7.5 Semester 2 60 40 1
CA688 Blockchain: Basics and Applications 7.5 Semester 1 40 60 1
CA688I Blockchain: Basics & Applications 7.5 Semester 2 60 40 1

Year 2 Optional Modules,
Code Title Credit Semester Exam % CA % Resit Category
CA6001I Developing Blockchain Systems 7.5 Semester 2 0 100 1
CA6002I Computer Security 7.5 Semester 2 60 40 1
CA6003 Practicum (Blockchain) 30 Autumn Semester 0 100 2
CA6005I Mechanics of Search 7.5 Semester 2 0 100 1
CA6006I Reinforcement Learn & MultiAgent Systm (NUIG) 5 Semester 2 70 30 3
CA6007I Knowledge Representation (NUIG) 5 Semester 2 70 30 3
CA6008I Tools & Tech for Large Scale Data Analyt-NUIG 5 Semester 2 70 30 3
CA642A Cryptography & Number Theory 7.5 Semester 1 75 25 1
CA642IA Cryptography and Number Theory 7.5 Semester 1 75 25 1
CA645A Network Security 7.5 Semester 2 70 30 1
CA646I P-Key Cryptography & Sec Protocols 7.5 Semester 1 75 25 1
CA650A Software Process Quality 7.5 Semester 2 75 25 1
CA652I Artificial Intelligence, Info & Info Seeking 7.5 Semester 2 50 50 1
CA660A Statistical Data Analysis 7.5 Semester 1 75 25 3
CA675A Cloud Technologies 7.5 Semester 1 100 1
CA681I Machine Translation 7.5 Semester 2 0 100 1
CA682I Data Management and Visualisation 7.5 Semester 1 75 25 3
CA683A Data Analytics and Data Mining 7.5 Semester 2 75 25 1
CA685 Data Analytics Practicum 30 Autumn Semester 0 100 2
CA688A Blockchain: Basics and Applications 7.5 Semester 1 60 40 1
CA689 Practicum (Artificial Intelligence) 30 Autumn Semester 0 100 2
CA694 Practicum (SSE Practicum) 30 Autumn Semester 0 100 2
CA699I Topics of AI 7.5 Semester 1 50 50 1
EE516I Blockchain Scalability 7.5 Semester 1 75 25 1
EE544 Computer Vision 7.5 Semester 2 75 25 1

Requirements
• For entry onto this programme, candidates must hold, a second class honours degree or higher in Computer Science, Computing or Computer Applications.
• International candidates who are non-native speakers of English must satisfy the University of their competancy in the English language.

Application Deadlines
Applications will be accepted on a rolling basis until the programme is full or until the following dates:

Closing date for EU applicants for part time and full time is 31st July 2024.
Closing date for Non EU applications for full time is 1st July 2024.
Applications are closed for Non EU Applicants for part time study
Note applicants who require a study visa for the purposes of studying at DCU, are advised to apply as early as possible.

Application Queries
For EU applicant queries, please visit https://www.dcu.ie/registry/eu-postgraduate-taught-admissions or email postgraduateadmissions@dcu.ie

For non EU applicant queries, please visit https://www.dcu.ie/registry/international-admissions-undergraduate-and-postgraduate or email internationaladmissions@dcu.ie

All applicants should apply via link above.

Here's a quick step by step guide if you need help with your application:

• Please submit certified academic transcripts for all years of study at college or university in original language*, with certified English translations. Where an applicant is in their final year of their undergraduate degree, please submit certified transcripts for all years completed to date.

Programming Language Experience - Please upload a statement about your experience with a programming language. You should provide an example of an occasion when you wrote a piece of code, how you approached the task and what the code delivered. Please also include a sample of your own code.

• If applicable, evidence of competence in the English language as per DCU entry requirements. Please see link http://www.dcu.ie/registry/english.shtml

• Where an applicant is in their final year of their undergraduate degree, please submit certified transcripts for all years completed to date

1 year full-time or
2 years part-time
Please Note: Part time lectures are scheduled between 4-7pm two evenings a week

The programme commences in September 2024

Careers
The MSc in Computing aims to help meet the demand from industry for recruitment of personnel with significant exposure to relevant, advanced topics in computing. This programme is suitable for both experienced professionals and recent graduates. It enables software professionals with a number of years' experience to improve proficiency across a range of key disciplines in the field and to update skills beyond the narrow remit of training courses. It also supports recent graduates of computing and cognate disciplines to gain specialised knowledge and skills for higher-level industry entry at an early stage in their careers.

The focused nature of the majors on our MSc in Computing will ensure that you are in a pole position to gain employment in a wide range of jobs in Ireland and overseas.

Graduates of this course have gained employment as a:
• Software Engineer
• Computer Programmer
• IT Project Analyst
• Performance Engineer
• Python/Java Developer
• Web Applications Developer
• Business Analyst
• Technical Analyst
• Technical Consultant
• Data Analyst

Our graduates have gone on to successful careers in leading companies including Google, Facebook, Hubspot, Intel, Apple, Amazon, Microsoft and much more.

Others have progressed to PhD research and gained further advancement and recognition.