Data Analytics

This course is being offered under the Springboard+ initiative for free for jobseekers and for a nominal fee for applicants in employment. See the Springboard+ tab for more information and to find out how to apply for this course.

This course aims to produce technically competent, innovative graduates that will become leading practitioners in the field of data analytics. Upon completion, graduates will be able to:
• Conduct independent research and analysis in the field of data analytics
• Implement a research idea using the latest industry practices
• Demonstrate expert knowledge of data analysis and statistics
• Critically assess and evaluate business and technical strategies for data analytics
• Develop and implement business and technical solutions for data analytics

The course is designed to accommodate those whose specific interests in data analytics may be of a more technical or a more business-focused nature. Students will gain exposure to product commercialisation issues associated with data analytics. The course is delivered by faculty and practitioners using academic research, industry-defined practical problems, and case studies.

Who is the course for?
This course is for graduates who have substantial technical and mathematical skills. Graduates from non-STEM disciplines (Science, Technology, Engineering, and Mathematics) that have not developed these skills will need to be able to demonstrate an aptitude for technical and mathematical problem solving.

Entry requirements

Academic Entry Requirements
A minimum of a level 8 (honours degree) qualification (2.2 or higher) or equivalent on the National Qualifications Framework. Applicants may be from a cognate/STEM or non-cognate background.

For candidates who do not have a level 8 qualification, the college operates a Recognition of Prior Experiential Learning (RPEL) scheme - meaning applicants who do not meet the normal academic entry requirements, may be considered based on relevant work or other experience. Non-English speaking applicants must demonstrate fluency in the English language as demonstrated by IELTS academic score of at least 6.5 or equivalent.

Technology Requirements
This programme has a BYOD (Bring Your Own Device) policy. Specifically, students are expected to successfully participate in lectures, laboratories and projects using a portable computer (laptop/notebook) with a substantial hardware configuration. Its minimal suitable configuration is 8GB of RAM (16GB are recommended); a modern 64-bit x86 multi-core processor (Intel i5 or superior); 250+ GB of available space in hard disk; WiFi card; and a recent version of Ubuntu, macOS, or Windows.

Assessment Info

The course will be assessed with a blend of project work and exams. This varies between modules but typically assessment is 50% continuous assessment and 50% exam. Please note that in some instances exams may take place in the daytime.

Subjects taught

Course Content
Year 1 / Semester 1
• Statistics for Data Analytics
• Database and Analytics Programming
• Career Bridge

Year 1 / Semester 2
• Data Mining and Machine Learning I
• Modelling, Simulation, and Optimization
• Business Intelligence and Business Analytics - Elective Modules Group 1
• Data Intensive Architectures
- Elective Modules Group 2
• Career Bridge

Year 1 / Semester 3
• Data Mining and Machine Learning II
• Data Governance and Ethics
• Domain Applications of Predictive Analytics
- Elective Modules Group 1
• Scalable Systems Programming
- Elective Modules Group 2
• Career Bridge

Note: Electives are designed to allow students gain specialised knowledge in Data Analytics related areas. Electives may have dependencies, by picking a particular elective in Semester 2, students may restrict themselves to a single choice of elective in Semester 3. For the current suite of electives, dependencies are:
• Elective Modules Group 1:
Business Intelligence and Business
Analytics -> Domain Applications of
Predictive Analytics
• Elective Modules Group 2:
Data Intensive Architectures -> Scalable Systems Programming

Relevant Employment / Placement can be undertaken within the course timeline, generally 2nd or 3rd semester, or commenced within 3 months of course completion.

Note that all modules count towards the final award classification.


1 Year, 3 semesters from September to December 2019, January to May 2020 and late May to August 2020.

Indicative Schedule
Please see the Springboard+ section for further information on the schedule of upcoming courses.

Note that exams can be scheduled during the morning, afternoon or evening Monday to Saturday.


This course is free for applicants who are unemployed and who meet the academic requirements. Applicants who are in employment pay 10% of the course fee.

To find out if you're eligible visit

Springboard+ is co-funded by the Government of Ireland and the European Social Fund as part of the ESF Programme for Employability, Inclusion and Learning 2014-2020.

Enrolment dates

This face-to-face course will start in January 2020.
Start Date: 21/01/2020
End Date: 31/12/2020

Post Course Info

Career Prospects
Ireland could create 12,750 to 21,000 jobs in data analytics according to the Forfás and the Expert Group on Future Skills Report. This course is focused on providing graduates with the ability to work in the data analytics sector and take advantage of this demand. Graduates will be capable of creating specialist start-ups or fulfilling roles such as Data Scientist, Business Intelligence Analyst, Knowledge and Informatics Engineer, Data Analyst and Data Mining Engineer.

More details
  • Qualification letters


  • Qualifications

    Postgraduate Diploma (Level 9 NFQ)

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