Online course Part-time course
Modular Diploma in Data Analytics

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Funded by the Government of Ireland as part of the July Stimulus Higher Education Provision

Diploma in Data Analytics Course Overview and Audience

Data Analytics is among a set of emerging and rapidly developing technologies termed Innovation Accelerators, which have been identified as being critical to the next wave of digitalisation. According to Gartner’s Hype Cycle 2019, over the next decade, data analytics and AI will augment workers’ efficiency, as companies rely on leading tech to beat out competitors. Learners who complete this course will be equipped with machine learning techniques that are an essential component of data analytics. The module builds on and draws from the Fundamentals of Statistics for Data Analysis which provides learners with the ability to identify the fundamental nature of a data analytical problem. Through Data Exploration and Preparation participants obtain in-depth understanding of the rationale for data exploration and the methods used to explore data, while Data Visualisation and Communication provides the skills needed to present a variety of different types and volumes data and to display directly the results of learning achieved in previous modules.

This modular course will appeal to a range of adult learners who may or may not be currently in employment and wish to up-skill, or returners and other groups who may wish to re-skill and are seeking to develop their knowledge in data analytics on a flexible modular basis. It will give those working in IT, marketing, finance and other industries a knowledge of data analytics which will help enable them to make better business decisions.

This CCT College Dublin modular course consists of modules from CCT’s Level 7 Diploma in Data Analytics for Business (60 credits) awarded by QQI. The modular course certificate is awarded by CCT and is not an NFQ award but successful completion of the modules will entitle the learner to a transcript confirming the ECTS attained and the NFQ level of those ECTS. Upon successful completion of the programme learners will be entitled to exemptions from the specified modules on the Diploma in Data Analytics for Business at CCT College Dublin. Successful completion of the programme may also entitle a learner to exemptions from alternative programmes, including those offered by other Higher Education Institutions, subject to entry requirements.

    • Fundamentals of Statistics for Data Analytics

The aim of this module is to provide the learner with understanding of:
1. Numerical and statistical tools used to describe and summarise data.
2. The utility and application of inferential statistical methods.
3. The purpose and limitations of regression analysis and modelling.
4. The laws of probability and their application to data analysis.
5. Software tools used for the analysis of business data

    • Data Exploration and Preparation

The aim of this module is to provide the learner with understanding of:
The importance of exploratory data analysis as an essential first step in the data analytical process. How to identify and handle missing and out-of-range data. Methods of encoding data for specific machine learning algorithms

    • Data Visualisation and Communication

The aim of this module is to provide the learner with understanding of:
1. The value of data visualisation as a means of offering rapid insights into large quantities of data.
2. The theory, concepts, techniques and processes of data representation and visualisation.
3. The types of data visualisation and their associated use cases.
4. The current range of software tools available for data visualisation

    • Machine Learning I

The aim of this module is to provide the learner with:
1. The role of machine learning as a tool to solve data analytics problems.
2. The purpose of data mining frameworks and their usefulness.
3. The distinction between supervised and unsupervised machine learning methods.
4. The difference between the two fundamental types of supervised learning – classification and regression.
5. Software tools used to solve classification and regression problems.

Those who are in employment/working : 

For eligible applicants who are currently in employment/working 90% of the tuition fees will be covered by the HEA and the remaining 10% is payable by the student or their employer.

  • The Course Tuition Fee is €2,200 so €220 euro is payable by the student or their employer

Those who are unemployed, formerly self-employed and ‘Returners’:

The course is free and 100% funded for eligible applicants who are unemployed, formerly self-employed or who are classified by the HEA as ‘Returners’ or ‘Homemakers’.


Admission to this modular programme is through one of the following:

Evidence of prior learning, including experiential learning,


Possession of an NFQ level 5 award, including leaving certificate, FET award, or equivalent.

In addition, all applicants must evidence competence in mathematics equivalent to O6 standard in leaving certificate and competence in the use of IT. Basic computer literacy is not sufficient for this programme.

International applicants whose first language isn’t English must demonstrate a minimum competency in the English Language of CEFR B2+.

Applicants are encouraged to apply for entry based on prior learning (RPL) or prior experiential learning (RPEL) in line with the College policy. The College will thoroughly assess applications received through RPL and RPEL to ensure that candidates are able to evidence learning to an appropriate standard – normally the framework level equivalent to the direct entry qualification requirement and demonstrate potential to succeed and benefit from the programme.

This course will be delivered online. Online activities can include live or pre-recorded lectures, independent learning and assessment activities such as research tasks, discussion forums, simulations, quizzes and e-portfolio work along with online group activities such as live classes, group project work, virtual labs and tutorials.


We are hosting a number of events in the lead up to the next academic year to give prospective students the opportunity to find out more about their course and the College.  At the moment these events are virtual and you can pre-register here.

You can also make a safe socially distanced 1-2-1 appointment with an Admissions Advisor in the College campus building here from 9am-5pm Monday to Friday.

Ready to start your journey?