PG Dip (Science) in Business Data Analytics

Our flexible business data analytics programme

Register your interest to be kept informed. The information below refers to our Autumn 2022 programme. 

Request information about this programme.

"*" indicates required fields

This field is for validation purposes and should be left unchanged.
Register your interest

Programme Type: Postgraduate Diploma in Science

Next Intake: To be announced

NFQ: Level 9
Duration: 36 weeks

Academic Validation: QQI

Professional Certification: Analytics Institute

About the
PG Dip (Science) in Business Data Analytics

We are currently developing a new, flexible blended learning master’s degree that will help you upskill or reskill to forge a career path in data analytics or business intelligence. Register your interest to be kept informed. The information below refers to our Autumn 2022 programme. 

This programme is for those who wish to pursue a career in areas related to data analysis and business intelligence. It will support anyone seeking to either upskill or reskill with a view to forging a career path within the data analytics industry, which was established over 20 years ago and is now undergoing rapid growth with the advent of new technologies such as data mining and machine learning.

The Postgraduate Diploma in Science in Business Data Analytics is a 60-credit award at NFQ Level 9 delivered over three 12-week semesters between September and June. This flexible blended learning programme helps students to forge a career path in data analytics or business intelligence.

We have partnered with the Analytics Institute, Ireland’s professional membership organisation for the data science and analytics industry, who will coordinate work placements and work-based projects for students with its 120+ member organisations. Our aim is to prepare a talented, highly-qualified cohort of work-ready graduates through the deployment of a carefully crafted range of targeted academic and industry-focused activities.

Business analytics, technology and data science are the three pillars of knowledge underpinning the programme. The field of data analytics intersects these knowledge domains, and the programme design reflects this. The programme design is informed by data and analytics thought leaders from higher education and industry and covers areas such as data science, probability modelling, statistical data analysis and essential industry skills such as applied business analytics and effecting successful projects.

Entry Requirements

This information refers to our Autumn 2022 programme.

Applicants must hold a minimum grade of Lower Second-Class Honours (2.2), or equivalent, in an honours bachelor’s degree at NFQ Level 8.

Students on this programme will originate from directly cognate disciplines including computer science, mathematics, statistics, engineering and technology. Applicants from partially cognate disciplines such as finance, accounting, business, etc. may be accepted as determined by the Programme Director following evaluation against established criteria.

English Language Proficiency

An applicant whose first language/primary mode of expression is not English will be required to produce evidence of English competence. The required proficiency level is B2+ or higher in the Common European Framework of Reference for Languages (CEFR).

Mathematical Proficiency

The programme requires students to have good numerical and statistical skills. As candidates can come from a diverse range of disciplines, essential foundational mathematics and statistics concepts are introduced in a two-week orientation programme. Online learning resources are also provided to students in mathematics or programming should they require it after they complete the orientation programme.

Applications for this programme are made through Springboard+.

This information refers to our Autumn 2022 programme.

The programme runs for three 12-week semesters between September and August. Live webinars, face-to-face tutorials and laboratory tasks will be scheduled in the evenings and on Saturdays. Students will meet each other and their lecturers/tutors face to face at a venue at least once a semester during each module. Average weekly class time totals 12 hours.

There will also be additional online learning where students will be required to complete additional readings, practical work and assignments. An estimate of the total weekly time requirement is 30 hours.

Programme Content

Online asynchronous sessions can be studied in the student’s own time and include presentations, videos, tasks and collaborative activities. Students will interact with their fellow students and lecturers/tutors in online discussion forums and also meet them in live online webinars (in lecturer classes, practical laboratory tasks and tutorials). During the programme, they will create digital artefacts, code solutions to problems, create advanced data visualisations and produce technical reports.

An extensive online library is available to support students in their studies.

Placement or Project

Students will also complete a 12-week placement or project. Those working in the industry will undertake a project. Placement is organised for those who need industry experience. The Analytics Institute will arrange placements for those who need them.

This information refers to our Autumn 2022 programme.

Semester 1

BDA101 Software Development for Business Data Analytics: 5 credits, 8 weeks
BDA102 Understanding Data: 10 credits, 12 weeks
BDA103 Applied Probability Modelling: 10 credits, 12 weeks

Semester 2

BDA104 Statistical Data Analysis & Inference: 5 credits, 8 weeks
BDA105 Data Mining & Machine Learning: 10 credits, 12 weeks
BDA106 Applied Business Analytics: 5 credits, 12 weeks
BDA107 Effecting Successful Projects: 5 credits, 12 weeks

Semester 3

BDA108A Placement: 10 credits, 12 weeks
BDA108B Project: 10 credits, 12 weeks

This information refers to our Autumn 2022 programme.

Application Fee


Tuition Fees

There are some free or 90%-funded places available for eligible participants, supported by the Human Capital Initiative Pillar 1 fund. For more details about eligibility criteria, explore the Eligibility web page on the Springboard+ website.

Applicants who do not meet the eligibility criteria for fee remission will also be considered as long as they meet the academic criteria. Such applicants will pay the full programme fee.

If you have any questions, please contact our Enrolment team at +353 (0)1 661 0168 (press 2) between 9 AM and 5 PM Monday to Friday. You are also welcome to email

Student Support

Support for Students

Administrative and technical support teams, faculty and tutors are committed to helping students meet academic and career goals while having a positive experience. Students can draw from a range of available support to navigate big and small questions.

Our Faculty

Our dedicated faculty are mentors and leaders who bring their rich experience to the learning environment at Hibernia College. They are educators, practitioners and active researchers. Common among all our faculty is their focus on students.

Student Experiences

Are you curious about studying at Hibernia College? Former PME students share their reasons for choosing Hibernia College, their prior experience, research and careers. ‘As a mother and mature student, the course’s flexibility and the use of blended learning made it possible for me to balance my studies with taking care of my family.’ – Therese Barrett

Take the next step