Data Preparation & Visualisation - Springboard+ Micro Credential

Springboard+ is co-funded by the Government of Ireland and the European Union. Please see www.EUFunds.ie



This Certificate in Data Preparation and Visualisation micro credential is designed in response to industry feedback for the provision of accredited professional development opportunities for those working in IT or technical roles.



Extensive exploratory data analysis and proper data preparation are a crucial first step in any data analysis process. The aim of this micro credential Certificate programme is to provide the learner with an in-depth understanding of the rationale for data exploration and the methods used to explore data programmatically with Python, a high level, low barrier, programming language.



The student also learns the importance of feature selection and dimensionality reduction and the bias-variance trade-off, the importance of the correct encoding of data and the usefulness of feature engineering as a means of representing complex functional relationships to machine learning models. The module also deals with the theory and application of data visualisation methods and transmission media, tailored for diverse audiences.



By incorporating basic programming skills in a hands-on practical integrated manner enables the learner’s ability to program but also reinforces the inseparable nature of programming within the field of Data analytics. This module also includes what is essentially an embedded ‘bootcamp’ of basic programming concepts to ensure a level playing field for all learners (facilitated through the use of a low entry barrier language: Python).



Award: Certificate in Data Preparation and Visualisation

Awarding Body: Quality & Qualifications Ireland (QQI)

Award Level: NFQ Level 9 Minor Award (10 ECTS)

On completion of the programme learners should have knowledge, skill and competence in:



• Basic programming principles and the importance of exploratory data analysis as an essential first step in the data analytical process.

• Methods of encoding data for specific machine learning algorithms. The value of data visualisation as a means of offering rapid insights into large quantities of data.

• The theory, concepts, techniques and processes of data representation and visualisation.

• The types of data visualisation and their associated cognitive load.

• The current range of software tools available for data visualisation.



In addition to the core programme content, learners will develop a range of transversal development skills which will include; critical analysis, advanced evaluation, problem solving and communication skills.

The direct entry route to the Certificate in Data Preparation and Visualisation requires applicants to evidence numerate, technical and analytical ability to a minimum of NFQ level 8 standard.



The following are accepted as appropriate evidence for direct entry:

a. An NFQ Level 8 major award, or higher, in the discipline areas of ICT/Computing, Business, Science or Engineering or cognate discipline



or



b. An NFQ Level 8 major award, along with relevant experience in the area of Data Analytics and/or professional certification, may also be considered



In both scenarios presented above, applicants will also be required to evidence ability in the application of mathematical concepts such as algebra, or spreadsheet analysis and formulas, database knowledge, for example, to a level 8 standard. This is essential to demonstrate applicants numerate, technical and analytical ability required to ensure capacity for the extent of mathematical and technical content related to the programme.



This programme is designed for individuals who have previous knowledge in computing, analytics or similar through professional experience and/or educational qualifications. This programme is not suitable for individuals with only basic computer literacy.



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



Applications are also welcome from individuals who do not meet the standard entry requirements but wish to apply for entry based on prior learning (RPL) or prior experiential learning (RPEL). 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 qualifications requirement and demonstrate potential to succeed and benefit from the programme. Applications submitted on this basis will be assessed in line with the College RPL policy.



All applications for admission onto this programme should include:

• Updated CV

• ID Verification (passport picture page copy)

• Attested original copies of degree qualification parchment

• Attested original copies of final degree transcript of results

• RPEL documentation as required by CCT

• Evidence of English Language proficiency scores if the applicant’s first language is not English (IELTS, TOEFL etc.)

Application for this programme must be made via the Springboard Courses website (see "Application Weblink" above).

12 weeks part-time.



For September 2026 the course will be offered on an evening/weekend blended learning basis through Springboard+. Typically, learners will attend two evenings per week (on-line) plus 1-2 Saturdays (on campus).



All students will be introduced to the CCT online learning environment as part of the induction to the programme and will have access to further support as required.



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. Completing the online elements of the programme each week is essential to successfully complete the programme. On campus activities can include small group tutorials, labs, project supervision, problem solving case studies, library research and seminars.

Next Intake Commencing: Week of 31st August 2026.

The programme has been designed to produce graduates with the attributes required of cybersecurity specialists and analysts today and the ability to continue to develop knowledge, skill and competence to remain competitive and employable in an ever-advancing discipline. On successful completion of the MSc in Cybersecurity, graduates will be well placed to progress to further study, including level 10 doctoral studies, subject to the requirements of the institution to which they apply.



Graduates of the MSc in Cybersecurity should be able to secure professional roles at intermediate and advanced positions in data analysis across all sectors of the economy and progress to leadership or research roles using skills related to those learned in the programme curriculum. Potential roles include but are not limited to: Information security analyst, secure application developer, cybersecurity tester, risk advisory on information security and forensics, cloud security analyst.