Course Summary
The MSc in Computer Science (Data Analytics) programme 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.
College Link
Career Sectors
This course prepares you for working in the Career Sectors below. Follow the links to get a fuller understanding of the sectors you are preparing for.
Entry Requirements
The minimum admission requirements for entry to the programme 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.
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 programmes satisfying the minimum entry requirement is not a guarantee of a place. Depending on the programme of study applications will be assessed based on academic grades and any work/life experience. Applicants may also be required to attend for interview.
Application Details
Applications for courses commencing in September 2025 will open in November 2024.
Commencement Date: September 2025
Fees
Fees €5,600 Total Fee
Fees (Non-EU) €21,750 Total Fee
Part-time
Fees €3,100 Per Year
The Student
Career Interests
This course is typically suited for people with the following Career Interests. If these interests do not describe you, this course may prepare you for work you may not find satisfying.
Investigative
The Investigative person will usually find a particular area of science to be of interest. They are inclined toward intellectual and analytical activities and enjoy observation and theory. They may prefer thought to action, and enjoy the challenge of solving problems with sophiscticated technology. These types prefer mentally stimulating environments and often pay close attention to developments in their chosen field.
Career Progression
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
Duration
Mode of Study Full Time
Method of Delivery On-Campus
Schedule
Students have the option to complete modules in 1 Year or 1.5 Years
Teaching hours will take place Monday to Friday. Attendance in the evening is required for some modules. In general students complete 30 ECTS in Semester 1 (Sept-Jan), 30 in Semester 2 (Feb-May) and 30 in Semester 3 (Sept-Jan) for the dissertation or Team Project.
Option to complete the dissertation over the summer period, allowing completion in a 12-month period (Sept to Sept calendar year)
2 years
Mode of Study Part Time
Method of Delivery Blended
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 with Saturdays per week or for three evenings per week in each semester.
TU060 will be delivered in a blended mode with majority of learning activities delivered online with 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.