Post Graduate Diploma in Science in Business Data Analytics (Autumn 2022)

Hibernia College (PGDSBDA)
Key Programme Details
Award

Postgraduate Diploma in Science in Business Data Analytics

NFQ Level

Level 9 About NFQ

Delivery Method

Blended

Mode

Full Time

ECTS Credits

60

Department

Computer Science and Data Analytics

General Information
Contact

Bruna Dalmaso

Email

admissions@hiberniacollege.net

Phone

01 661 0168 ext 3

Address

Hibernia College
Block B
The Merrion Centre
Merrion Road
Dublin 4
D04 H2H4

Important Dates
Application Deadline

25/07/2022

Start Date

19/09/2022

End Date

31/07/2023

About this Course

The Postgraduate Diploma in Science in Business Data Analytics is designed to meet a skills shortage of data analysts and related occupations, both here in Ireland and internationally. This programme will be offered as a 60 credit, NFQ Level 9 blended learning programme where students will engage in a variety of face-to-face and online environments. This is a flexible, blended learning programme for those with busy lives. Average weekly class times are 10 hours. Live webinars, face-to-face tutorials and laboratory tasks are scheduled in the evenings and on Saturdays.

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 also includes a work placement or work-based project elective that allows students to synthesise the knowledge, skills and know-how developed in the earlier modules. The 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.

We have partnered with the Analytics Institute, Ireland's professional membership organisation for the data science and analytics industry. The Analytics Institute will coordinate work placements and work-based projects for students with its 120+ member organisations.

Objectives

On completion of the programme, students will be able to:

1. Demonstrate a critical understanding of the increasingly impactful role played by analytics across a wide range of sectors within a modern economy

2. Display mastery of essential analytics, technical and investigative skills, and demonstrate a capacity for self-improvement through learning new and more advanced skills of this nature

3. Identify and deploy a range of instruments to present and explain complex ideas and influence different audiences while being cognisant of their specific needs and requirements

4. Demonstrate a creative and imaginative skill set by analysing problems within a real-world or simulated setting and designing, testing, reflecting, reviewing and producing optimal solutions

5. Collaborate professionally within cross-functional, multi-discipline teams and provide advice and leadership where necessary and as appropriate to deliver impactful analytics output

6. Assimilate knowledge, ideas and concepts from related, complex analytics domains, thereby developing a wider and deeper understanding of the domain

7. Reflect on technologies, select optimal tools and apply analytics and related knowledge disciplines in the formulation and construction of solutions to complex problems

8. Improve personal performance through a combination of considered self-reflection and self-analysis, respond professionally to feedback and have the capacity to deliver open and constructive feedback to others

9. Make and justify informed scientific-based decisions, paying particular regard to balancing creativity, logic and evidence while recognising and addressing other constraints

10. Demonstrate knowledge of relevant research methodologies and apply this knowledge ethically when tackling complex challenges relevant to the analytics domain

Entry Requirements

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.

A minimum grade of Lower Second-Class Honours 2.2, or equivalent, in an honours bachelor's degree at NFQ Level 8.

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 learners in mathematics or programming should they require after they complete the orientation programme.

Long Description

The programme runs for three 12-week semesters between September and June. Average weekly class times are 10 hours made up of blended live webinars, face-to-face tutorials and laboratory tasks and additional on-demand online learning.

Live webinars and face-to-face tutorials will be scheduled in evenings and on Saturdays.

Students will be required to complete additional readings/practical work and assignments.

An estimate of the total weekly time requirement is 30 hours.

Semester 1

DA1X1 Understanding Data: 10 Credits, 12 Weeks

DA1V2 Software Development for Business Data Analytics: 5 Credits, 4 Weeks

DA1X3 Applied Probability Modelling: 10 Credits, 8 Weeks


Semester 2

DA2X1 Data Mining & Machine Learning: 10 Credits, 12 Weeks

DA2V2 Statistical Data Analysis & Inference: 5 Credits, 4 Weeks

DA2V3 Applied Business Analytics: 5 Credits, 8 Weeks

DA2V4 Effecting Successful Projects: 5 Credits, 12 Weeks


Semester 3

DA3X1 Placement: 10 Credits, 12 Weeks

-or-

DA3X2 Project: 10 Credits, 12 Weeks

Why Choose This Course

Hibernia College's flexible blended delivery model makes it possible to get a Level 9 qualification. We offer live webinars, face-to-face tutorials and laboratory tasks in the evenings and on Saturdays

The 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 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.

Students will get the opportunity to engage with innovative data companies on relevant, impactful projects as part of their experiential learning.

Career Opportunities

Graduates of the programme will be work-ready and have the essential skills, knowledge, and know-how to fill a range of data analytics careers in a wide range of fields including finance, health, agriculture, tourism, entertainment and hospitality.

Specific career opportunities include data analyst, data scientist, data architect, data engineer, big data management scientist, machine learning engineer, business intelligence analyst, business systems analyst, logistics analyst and marketing analyst.

Graduates will also be prepared to work in the key intelligent data analytics research fields such as search and analysis (i.e., data mining), semantic processing, cognitive systems and predictions (i.e., machine learning), visualisation, representation, benchmarking and evaluation.

Timetable Info

Timetable to be confirmed

Delivery Location

Blended

Delivery Notes

The modules are delivered through a flexible, blended learning approach that combines online and face-to-face elements.

The programme runs for three 12-week semesters between September and June. Average weekly class times are 10 hours made up of blended live webinars, face-to-face tutorials and laboratory tasks and additional on-demand learning.

Live webinars and onsite tutorials will be scheduled in evenings and on Saturdays.

Students will be required to complete additional readings/practical work and assignments. An estimate of the total weekly time requirement is 30 hours.

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

The students will meet each other and their lecturers/tutors face-to-face at a venue at least once for each module. They will also complete a 12-week placement or project where they will be supported by an academic supervisor and employer supervisor (for placement). During the programme, they will create digital artefacts, code solutions to problems, create advanced data visualisations and produce technical reports.

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.

Admissions Contact Details
Contact Person

Bruna Dalmaso

Address

Hibernia College
Block B
The Merrion Centre
Merrion Road
Dublin 4
D04 H2H4

Phone

01 661 0168 ext 3

Email

admissions@hiberniacollege.net

RPL Information

https://qualityframework.hiberniacollege.com/document/view-current.php?id=22

Application Procedures

Apply online at www.springboardcourses.ie.

Applicants can submit their online application while gathering the required supporting documentation.

All applicants will be required to provide transcripts of their level 8 degree, a copy of their passport and refer to https://springboardcourses.ie/eligibility for their category and provide the evidence as outlined. The Admissions Team will assist in advising of the documentation required once an application has been received.

The deadline for applications is subject to change and is at the discretion of Hibernia College

Media
Postgraduate Diploma in Science in Business Data Analytics

The programme brochure contains full information about the programme, structure and learning outcomes.

https://hiberniacollege.com/business_data_analytics_brochure_2022/


Webinar Event

We will be holding an information webinar on Thursday 26th May. Register below if you wish to attend

https://hiberniacollege.com/hibernia-college-events/