Computer Science - Data Science - Grangegorman

What is Computer Science (Data Science)?

The MSc in Computer Science (Data Science) course aims to produce graduates with the knowledge and skills to work with large amounts of raw data and extract meaningful insights from it. Graduates are equipped with deep technical skills (in data management, data mining, probability and statistics, and machine learning), but also with the softer skills (in communications, research and problem solving) required to work effectively within organisations.

Specialist Core Modules

• Probability & Statistical Inference

• Machine Learning

• Working with Data

• Data Management

• Data Mining

• Data Visualisation



Critical Skills Core Modules

• Research Writing & Scientific Literature

• Research Methods and Proposal Writing

• Research Project & Dissertation



Option Modules (Two required)

• Geographic Information Systems

• Universal Design

• Programming for Big Data

• Problem Solving, Communication and Innovation

• Social Network Analysis

• User Experience Design

• Security

• Deep Learning

• Speech & Audio Processing

• Linear & Generalised Regression Models



Students can also take specialist core modules from the Data Science stream as optional modules, subject to availability and schedules.

Minimum Entry Requirements?

The minimum admission requirements for entry to the course are a B.Sc. (Honours) in Computer Science, Mathematics or other suitably numerate discipline with computing as a significant component. The degree should be at the level of Honours 2.1 or better or at Honours 2.2 or better with at least 2 years of relevant work experience. Applicants with other qualifications at Honours 2.1 or better level and relevant experience may also be considered.



If English is not your first language you will need to provide evidence of your English language proficiency as detailed on our website. Applicants must present a minimum IELTS English proficiency score of 6.5 overall with at least level 6.0 for each component.



Note: Due to the considerable competition for our postgraduate courses satisfying the minimum entry requirement is not a guarantee of a place. Depending on the course of study applications will be assessed based on academic grades and any work/life experience. Applicants may also be required to attend for interview.

Applications for full-time and some part-time postgraduate programmes commencing in September 2026 are now open. Applications for the remaining part-time programmes will open in February 2026. Please check individual course pages for application details.

2 years part-time, blended delivery.



Schedule

Teaching will be in the evening with classes starting at 18.00. Some critical skills modules are scheduled on a Saturday. Part-time students can progress through the course at their own pace. The recommended pathway to complete the part-time course in 2 years requires either taking modules two evenings (and 3 Saturdays per term) or for three evenings per week in each semester.



This course will be delivered in a Mixed (Online & On Campus) mode: the majority of learning activities are delivered online. There is also the option of a number of onsite face-to-face touch points in each semester. These touch points include the induction event at the beginning of the academic year and face-to-face lectures and lab in weeks 1, 7 and 13 of each semester. In order to facilitate students who cannot attend, each face-to-face activity will be accompanied by an online version of the event, lectures and labs will be livestreamed from the classroom.

Commencement Date: September 2026.

What are my career opportunities?

Data analytics has been highlighted in a range of recent reports as an area of strategic importance both nationally and internationally. Areas in which opportunities for data analytics practitioners exist include retail, financial services, telecommunications, health, and government organisations. Specific roles include but are not limited to:



Data Analytics Consultant

Data Scientist

Data Analyst

Data Architect

Database Administrator

Data Warehouse Analyst

Business Intelligence Developer

Business Intelligence Implementation Consultant

Business Analyst

Reporting Analyst