Computer Science - Data Science - Grangegorman

TU Dublin - Technological University Dublin

Computer Science - Data Science - Grangegorman

Subjects taught

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.

Entry requirements

Minimum 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 dates

Applications for courses commencing in September 2024 will open in November 2023.


1 Year or 1.5 Years
Mode of Study Full Time
Method of Delivery On-Campus

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

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.

Enrolment dates

Commencement Date: September 2024

Post Course Info

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

More details
  • Qualification letters


  • Qualifications

    Degree - Masters (Level 9 NFQ)

  • Attendance type

    Blended,Evening,Part time,Daytime,Full time

  • Apply to

    Course provider