Program start date | Application deadline |
2025-09-01 | - |
Program Overview
Postgraduate Diploma in Big Data Management and Analytics
Overview
The Postgraduate Diploma in Big Data Management and Analytics is a 1-year programme, delivered on two evenings per week and Saturdays. Building upon students' knowledge of computing science with the aim to create big data specialists, as a graduate of this course, you will:
- Obtain specialist knowledge and skills essential for a career in Big Data Management and Analytics.
- Establish an analytical mindset necessary for independent academic and professional research.
- Gain a practical understanding of the appropriate design and implementation strategies used in the development of Big Data solutions.
- Develop a team player attitude necessary to communicate problems, ideas and solutions to all levels of the industrial team.
- Build upon your knowledge of supporting topics in the area of Computing Science.
Course Highlights
- Emerging discipline with huge job opportunities
- Develop highly sought-after skills
- Fully aligned with industry needs
- Access to innovative tools and technologies
- A dedicated experienced lecturing team
Intake Dates
- Dublin - Full-Time - September 2025
Course Details
This programme contains eight taught modules, four of which are delivered over each of the two semesters.
Modules
Big Data Analytics
This module aims to equip the learner with a range of most relevant topics that pertain to contemporary analysis practices, and are foundational to the emerging field of big data analytics. Learners are guided through the theoretical and practical differences between traditional datasets and Big Data datasets. An overview of the initial collection of data will be explored for multiple data sources. A formal grounding in analytical statistics is a major part of the module curriculum. Learners are expected to apply principles of statistical analytics to solve problems and inform decision making. Learners achieve this through developing knowledge and understanding of statistical analytics techniques and principles while applying these techniques and principles in typical real world scenarios.
Information Retrieval and Web Search
This module introduces the learner to the concepts of information retrieval (IR) and web search. They encounter various techniques used in IR and means of evaluating their performance. Learners also gain an exposure to the practical design of large- scale IR systems that are commonly used in the web search domain. Current trends in IR, such as collection and data fusion are introduced through the use of academic papers.
Concurrent and Parallel Programming
The future of microprocessor development is based around multiprocessor multicore architectures that will deliver the performance required for future application demands. The difficulty for software developers is to write programmes that harness the power of these new architectures. As a result, the fundamental aim of this module is to teach the learner how to write software for these machines.
Cloud Computing
This module aims to introduce the learner to the concept of cloud computing and how it differs from the client server model of computation that is seen on the web today. Cloud computing applications are charged on a per use basis i.e. clients only pay for what they have used. Many companies are using cloud computing to offload some of their work onto these clouds as a means of saving on software and hardware cost.
Big Data Management
This module aims to equip the learner with the skills to implement, from the batch to the speed layer, an end-to-end Big Data storage system using the most current technologies. As a grounding to the subject area, the learner will be guided through an overview of the traditional approach of data storage and access, with all theory grounded in real-world technological examples. As technologies have progressed, the availability of data has increased dramatically. The volumes of data dealt with in modern systems are far beyond what traditional systems can handle. During this module, the main failure points of traditional systems with regard to this level of data will be explored. Each layer of the Lambda Architecture will be explored in detail from theory through to implementation via current technologies.
Data Mining Algorithms and Techniques
This module aims to give learners a thorough understanding of different data mining techniques, algorithms and tools necessary to infer information from large datasets. The learners will understand the underpinning concepts and principles that make these algorithms work. The learners will encounter and will implement various data mining techniques.
Applied Data Science
This module aims to introduce the learner to the fundamental principles of data science and equips them with “data-analytic thinking” necessary for extracting useful knowledge and business value from the relevant datasets. The module introduces the learner to the principles underpinning the processes and strategies necessary to solve real-world problems through data science techniques. The module focuses on data science concepts as applied to practical real-world problems and aims to teach learners the underlying concepts behind data science and most importantly how to approach and be successful at problem-solving. Problem-solving and information discovery strategies will be developed via in-depth analysis of existing Big Data implementations and case studies. As most of the information discovered from large datasets is of direct use to business decisions, both reporting and visualization are an important element of this module.
Research Methods
This module serves to significantly deepen the learner's research skills, both in relation to the module related assignments and later in the completion of a dissertation/dissertation by practice. Specifically, it extends the ability of self-directed learners by equipping them with the appropriate vocabulary for reflecting on, critiquing and evaluating their own work and that of others. Throughout the module, learners are required to engage in a number of research methodologies and current research issues and trends in computing science. The module also addresses the need for good project management skills and techniques for the successful delivery of any project.
Timetables
Provisionally, the course will be held on Tuesday and Thursday evenings, and during the day on Saturdays.
Entry Requirements
Candidates applying for this course should have a 2.2 Level 8 honours degree in Computing Science, or a 2.2 Higher Diploma in Computing or related discipline or international equivalent and/or relevant work experience. Those that have relevant work experience may be required to attend a virtual meeting with the Programme Director to establish suitability.
Tuition Fees
Irish/EU citizens, living in Ireland
- Study Mode: Full-Time Dublin: EUR 6,550.00
- Study Mode: Part-Time Dublin: EUR 6,550.00
An Academic Administration Fee of €250 is payable each September at the start of term. For students starting in the January/February term, €125 is payable in February, and then €250 will be payable each September from then onwards.
A 2% Learner Protection Charge is applicable each academic year in addition to the fees quoted. The fees above relate to Year 1 fees only.
Progression
Graduates of the Postgraduate Diploma in Science in Big Data Management and Analytics course have the option to continue their studies at Griffith College by progressing to the MSc in Big Data Management and Analytics.
Program Outline
Postgraduate Diploma in Big Data Management and Analytics
Degree Overview:
- This one-year level 9 postgraduate diploma equips students with the knowledge and skills to become independent, critically-minded big data specialists.
- The program addresses the growing demand for big data professionals in the industry.
- The curriculum focuses on developing:
- Specialist knowledge and skills in big data management and analytics.
- Analytical mindset for independent research.
- Teamwork and communication skills to effectively collaborate and communicate within the industry.
- Additional knowledge in supporting topics in the field of computing science.
Outline:
- The program comprises eight taught modules, four delivered in each semester.
- Modules include:
- Big Data Analytics
- Information Retrieval and Web Search
- Concurrent and Parallel Programming
- Cloud Computing
- Big Data Management
- Data Mining Algorithms and Techniques
- Applied Data Science
Teaching:
- The program is delivered through a combination of lectures, tutorials, and practical exercises.
- The teaching faculty comprises experienced lecturers with expertise in big data management and analytics.
- The program utilizes innovative tools and technologies to enhance the learning experience.
Careers:
- Graduates of the program are well-positioned for careers in big data analytics, data science, and related fields.
- Potential career paths include:
- Data Analyst
- Data Scientist
- Big Data Engineer
- Business Intelligence Analyst
- Research Scientist
Other:
- The program is designed to be fully aligned with industry needs.
- Students have access to state-of-the-art facilities and resources.
- The program is offered on a full-time basis and requires attendance two evenings per week and Saturdays.
- Two intakes are offered per year, commencing in September and February.
Tuition Fees Irish/EU citizens, living in Ireland Irish/EU citizens, living in Ireland Study Mode: Full-Time Dublin: EUR 6,400.00 Study Mode: Part-Time Dublin: EUR 6,400.00 An Academic Administration Fee of €250 is payable each September at the start of term. For students starting in the January/February term, €125 is payable in February, and then €250 will be payable each September from then onwards. A 2% Learner Protection Charge is applicable each academic year in addition to the fees quoted. The fees above relate to Year 1 fees only. Flexible payment options Sponsorship Post: Student Fees, Griffith College Dublin, South Circular Road, Dublin 8 Email: accounts@griffith.ie 2% Learner Protection Charge All QQI accredited programmes of education and training of 3 months or longer duration are covered by arrangements under section 65 (4) of the Qualifications and Quality Assurance (Education and Training) Act 2012 whereby, in the event of the provider ceasing to provide the programme for any reason, enrolled learners may transfer to a similar programme at another provider, or, in the event that this is not practicable, the fees most recently paid will be refunded. QQI Award Fee Please note that a QQI Award Fee applies in the final year of all QQI courses.
Griffith College
Overview:
Griffith College is a private higher education institution with campuses in Dublin, Cork, and Limerick. It offers a wide range of undergraduate and postgraduate programs, with a focus on providing students with a flexible and supportive learning environment.
Services Offered:
Flexible Study Modes:
Griffith College offers a variety of study modes, including full-time, part-time, blended, and online learning options.City Centre Campuses:
Students can benefit from the convenience of studying in the heart of Dublin, Cork, and Limerick.Global Community:
Griffith College boasts a large alumni network, connecting graduates across the world.Student Life and Campus Experience:
Key Reasons to Study There:
Small Class Sizes:
Griffith College prides itself on its low lecturer-to-student ratio, allowing for personalized attention and support.Diverse Course Options:
Students have a wide selection of courses to choose from, with over 200 programs available.City Centre Locations:
The college's campuses offer easy access to city amenities and cultural experiences.Academic Programs:
Faculties:
Griffith College offers programs across various faculties, including Business, Arts, Science, and Technology.Springboard+:
The college provides Springboard+ programs, which are government-funded upskilling and reskilling initiatives.Postgraduate Programs:
Griffith College offers a range of postgraduate programs, including Masters and PhD degrees.Other:
Open Days:
Griffith College hosts open days to allow prospective students to explore the campuses and learn more about the institution.News & Events:
The college regularly updates its website with news and events, showcasing student achievements and highlighting key developments.Entry Requirements:
EU Home Students:
- A 2.2 Level 8 honours degree in Computing Science, or
- A 2.2 Higher Diploma in Computing or related discipline, or
- International equivalent
- Applicants with relevant work experience may be required to attend a virtual meeting with the Programme Director to establish suitability.
International Overseas Students:
- Follow the same entry requirements as EU Home Students. However, it is likely that they will need to demonstrate proficiency in English, as the course is taught in English.
Additional notes:
- This program is offered in a full-time mode.
- There are two intakes per year, commencing in September and February.