Expand+This information refers to our Autumn 2022 intake. It will be updated for any future intakes. If you would like to be notified, please enter your details and we will contact you when we have further information.

A minimum grade of Lower Second-Cla...

Hide-This information refers to our Autumn 2022 intake. It will be updated for any future intakes. If you would like to be notified, please enter your details and we will contact you when we have further information.

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 will be introduced in the two-week orientation programme. Online learning resources will also be provided to students in mathematics or programming should they require it after they complete the orientation programme.

Expand+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....

Hide-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 will cover areas such as data science, probability modelling, statistical data analysis and essential industry skills such as applied business analytics and effecting successful projects.

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.

This information refers to our Autumn 2022 intake. It will be updated for any future intakes. If you would like to be notified, please enter your details and we will contact you when we have further information.

The programme runs for three 12-week semesters between September and June. 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 during each module. Average weekly class time totals 10 hours.

There will also be additional on-demand 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.

Course 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 fora and also meet them in live online webinars 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 will be 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.

HOW FLEXIBLE IS THE PROGRAMME?
The Postgraduate Diploma in Science in Business Data Analytics is a flexible programme, but requires a full-time commitment.

Average weekly class times total 10 hours. Live webinars, face-to-face tutorials and laboratory tasks, 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 during each module.

There will also be additional on-demand 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.

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.

Expand+Semester 1
BDA101 Software Development for Business Data Analytics: 5 Credits, 4 Weeks
BDA102 Understanding Data: 10 Credits, 12 Weeks
BDA103 Applied Probability Modelling: 10 Credits, 8 Weeks

Semester 2
BDA104 Statistical Data Analysis & Infe...

Hide-Semester 1
BDA101 Software Development for Business Data Analytics: 5 Credits, 4 Weeks
BDA102 Understanding Data: 10 Credits, 12 Weeks
BDA103 Applied Probability Modelling: 10 Credits, 8 Weeks

Semester 2
BDA104 Statistical Data Analysis & Inference: 5 Credits, 4 Weeks
BDA105 Data Mining & Machine Learning: 10 Credits, 12 Weeks
BDA106 Applied Business Analytics: 5 Credits, 8 Weeks
BDA107 Effecting Successful Projects: 5 Credits, 12 Weeks

Semester 3
BDA108A Placement: 10 Credits, 12 Weeks
-or-
BDA108B Project: 10 Credits, 12 Weeks