The MSc in Computing (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.
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.
Specialist Core Modules
Probability & Statistical Inference
Working with Data
Critical Skills Core Modules common to all MSc in Computing specialisms
Research Writing & Scientific Literature
Research Methods and Proposal Writing
Option Modules (Two required)
Geographic Information Systems
Programming for Big Data
Problem Solving, Communication and Innovation
Social Network Analysis
User Experience Design
Speech & Audio Processing
Linear & Generalised Regression Models
Students can also take specialist core modules from the other MSc streams as optional modules, subject to availability and schedules.
Details on the delivery structure and module descriptions are available here.
Students can choose to exit with a Postgraduate Certificate on completion of 30 ECTS or a Postgraduate Diploma on completion of 60 ECTS.
Students who wish to complete an MSc dissertation to achieve the MSc award will be eligible to do so on successful completion of all core modules and the achievement of an average grade of at least 50% over all core modules.
School of Computer Science
College of Sciences and Health
DIT Kevin Street, Dublin 8
School Contact: Andrea Curley
T: 01 402 4950/2840
Who Should Apply?
Individuals with experience developing or using data intensive systems who wish to extend their skills to include advanced analytics techniques and join an industry niche of massive growing importance.
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.
Timetable / Hours
For the full-time route, 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. For those who wish to complete in one calendar year the dissertation can be taken over the summer months (June-Sept) with approval.
For the part-time route, teaching will be in the evening with classes starting at 18.30. Attendance on Saturdays is required for some critical skills modules. Part-time students can progress through the programme at their own pace. The recommended pathway to complete the part-time programme in 2 years requires either attendance for two evenings with Saturdays per week or for three evenings per week in each semester.
*The fees outlined for each course are provisional and are subject to change
For information on full time postgraduate fees please see link.
For information on part time postgraduate fee please see link.
For information on funding please see the following link: Fees and Funding