NCI Course Details

The course structure accommodates a wide audience of learners whose specific interests in data analytics may be either technically focused or business focused.

All students will also gain exposure to pertinent legal issues and product commercialisation considerations associated with the data analytics field. The course will be delivered using academic research, industry-defined practical problems, and case studies. This approach will naturally foster a deeper knowledge of the subject area and create transferable skills for work such as critical thinking, problem-solving, creative thinking, communication, teamwork, and research skills. The course is completely delivered by faculty and industry practitioners with proven expertise in data analytics.

This course is ideal for graduates that are looking to progress into the emerging data analytics market to increase their employment potential. The course is suitable for graduates who have technical or mathematical problem-solving skills

Graduates from disciplines that have not developed these skills will need to be able to demonstrate an aptitude for technical or mathematical problem solving.

Core Modules

• Statistics for Data Analytics
• Database and Analytics Programming
• Data Mining and Machine Learning I
• Data Mining and Machine Learning II
• Modelling, Simulation and Optimization
• Research in Computing
• Data Governance in Ethics
• Research Project

In addition, there is a choice of two electives

Elective Choices

• Business Intelligence and Business Analytics
• Data Intensive Architectures
• Domain Applications for Predictive Analytics
• Scalable Systems Programming

There are dependencies between electives modules. The first elective module choice will dictate the choice of the second elective module. For the current suite of electives dependencies are:

• Business Intelligence and Business Analytics -> Domain Applications for Predictive Analytics
• Data Intensive Architectures -> Scalable Systems Programming.

Elective modules are subject to availability and a minimum number of students required to run a module.

Assessment

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, evenings and at weekends.

For information on the modules taught on this course view our module descriptors. Elective modules are subject to availability.

The Master of Science in Data Analytics is awarded by QQI at level 9 on the National Framework of Qualifications. Students who successfully complete this course may progress to a major award at level 10 on the NFQ. Students may also elect to exit early with the Postgraduate Diploma in Science in Data Analytics at level 9 on the NFQ. Please note that international students do not have the option to exit this programme early. 

A minimum of a level 8 (honours degree) qualification (2.2 or higher) on the National Qualifications Framework. Applicants may be from a cognate/STEM background. Standard applicants for the programme are those holders of computing or numerate degrees.

All applicants for the programme must provide evidence that they have prior programming experience (e.g. via academic transcripts or recognised certification).

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 an IELTS academic score of at least 6.0 or equivalent. Visit our English Language Requirements page for further information.

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. The 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.

It is the responsibility of each student to ensure their computer is functioning correctly and that they have full administrator rights. NCI IT cannot provide support for these personal devices.

A Laptop Loan Scheme is available for eligible students to assist them in completing their chosen course.

Students need to be available 09.00-18.00 Mon – Fri (class days and times vary).

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

Classes will take place face-to-face on campus.

This course runs over 1 year; 3 semesters with a final research project.

€6,800 total fee

(EU/Ireland applicants, fees revised annually)

You can spread the cost of this course with a direct debit plan.

NCI Scholarships

We offer a range of scholarships and discounts for both NCI and non-NCI graduates. Current NCI students and alumni can avail of a 25% discount on this course by applying for our Master's Edge Scholarship.

View our full list of scholarships.

The fee for this course for international students is €15,000.

Find out more about studying at NCI as an international student.