Postgraduate Diploma in Applied Data Science
Course Overview
The 60-credit, level 9, Post-graduate Diploma (PGDip) in Applied Data Science will be delivered over 3-semesters. It will commence in mid-January 2025 with an onsite 1-2 day programme induction and boot-camp. Weekly classes will be delivered online, through a combination of live interactive evening classes and asynchronous contact (materials and activities). There will be one additional contact day per semester.
The PGDip in Applied Data Science builds strong foundational skills, in statistics, statistical programming using python and applied database systems and more advanced knowledge of data analytics methodologies, applied data architecture and data visualization techniques. It also develops the learners understanding of modern machine learning and artificial intelligence algorithms and an awareness of their applications and future potential, alongside ethical awareness, an appreciation of the importance of strong data governance and the capability to conduct independent applied research.
Students will build advanced digital competencies, develops critical thinking, research capability and specialist analytical skills together with business acumen, in an area highlighted as crucial for the workplace of the future. The programme is underpinned by industry driven problems taken directly from domains in which learners are expert. This is achieved both within modules, through case studies and data-driven projects, but also through the final focus on an Applied Industry Project. Cross-modular projects, between modules such as Statistics and Programming for Data Analytics, add to the applied focus.
The programme culminates in a 10-credit Applied Industry Project module, which will give students the opportunity to apply the knowledge and technical skills gained in data analytics, machine learning and artificial intelligence, in an applied setting – either in the student's current employment setting (if appropriate), through a work placement or via an applied industry relevant project.
Learners' analytical, critical thinking and problem-solving abilities will be forged in the identification of an appropriate project proposal framed to address a specific industry need and capable of adding value to an organization. Their investigative, research and project management abilities will be employed throughout the various stages of a data analytics project – from framing a research question, through identify, managing and storing appropriate data, to analysing, modelling and interpreting outputs and finally to visualising and communicating findings for a diverse audience. Thus, allowing the leverage of data to gain insights that can inform decision-making.
What makes this course different
In-Demand Graduates
Skills shortages in the area of data analytics has been highlighted by the Department of Enterprise, Trade and Employment, with "statisticians specialising in big data analytics with skills in IT, data mining, modelling, and advanced maths or related and relevant specialist skills" listed as a critical skill.
Career Opportunities
Graduates from the Postgraduate Diploma in Applied Data Science will be positioned to take on different roles in a variety of sectors. The typical roles that a graduate would work in are as follows: Data Analyst, Data Scientist, Data Architect, Business Analytics Specialist, Data Visualisation Developer, Analytics Manager or Statistician.
These career paths can take place in many different sectors and industry including areas such as research; academia; business; finance; banking; healthcare; pharmaceuticals; commercialism; and technology, and this list is not exhaustive as roles such as these are available in any sector that collects data.
With the increase in information available via the internet and the increase in storage capacity, the amount of data that is being collected and stored has exploded. Therefore, the need for graduates with these skills is ever increasing and these courses provide an excellent opportunity for employed and unemployed graduates to upskill and to become more sought-after by employers.
Course Delivery and Modules
The PG Dip in Applied Data Science will commence on the week of Jan 20th 2025 with a 1-day onsite induction programme and boot-camp.
This programme will be delivered over 3 semesters (Jan25-May 25, Sept25 - Jan26, Jan26 - May26) with online synchronous (live) classes delivered two evenings per week, typically Mondays and Wednesdays 6 pm to 9pm, attendance / participation in all live classes is important as this will be necessary to master the material. Live online classes will be support by additional asynchronous activity (videos/ resources and tasks) which participants complete in their own time within the same week. There will be an additional 1-2 days (Saturdays) of live class per semester - which will be facilitated onsite and online.
Participants will be required to attend onsite for any formal end-of-semester examinations, these will occur during examination periods in Jan/ May, but students will be advised well in advance.
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Semester 1:
- Statistics (10 credits): Will build fundamental knowledge in statistics and probability and apply these techniques to many datasets as the foundations to build further knowledge in the area of data analytics. This module works in harmony with the Programming module to ensure the students can apply the analytical and modeling techniques to real data, which is showcased in the cross modular project at the end of the first semester.
- Programming for Data Analytics (10 credits): This module will teach students about data structures and programming techniques which will allow them to gather, manipulate, store and graph data sets from a variety of case studies. The programming language used will be python, which provides numerous of libraries relevant to Data Analytics. This module runs in parallel with Statistics and culminates in a cross-modular Data project.
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Semester 2:
- Research Methods with Ethics and Data Governance (5-credits): This module will give an overview of all the major ideas in applied data analytics research methodology, both classic and new. It will cover the fundamentals of both qualitative and quantitative research, as well as how to comprehend statistics without the need of computations, making it appropriate for all students. It will employ lively examples on current ethical and governance concerns to pique students' attention and engage them by demonstrating the relevance of ethics, governance and research methodologies to their daily lives. Many case studies and in-class exercises will aid improved student comprehension and promote classroom debate. It also discusses the primary data governance models used in data analytics and artificial intelligence.
- Data Visualisations (5 credits): This module will enable the student to develop the advanced technical, critical thinking and communication skills required to explore and present real-time, dynamic and static data to deliver valued insights to a targeted audience within the context of a data analytics project lifecycle.
- Applied Database Systems (5 credits): This module introduces students to the principles and usage techniques of relational database systems. Students will connect to and query data from local and remote relational databases for basic analytics workloads from their application layer code. Multi-table database design will include datatype specification and normalisation to 3NF. Advanced database features such as views, transaction handling, concurrency, stored procedures, triggers, full-text search and foreign data wrappers will be introduced. Limitations of the relational model will be explored and non-relational stores introduced.
- Machine Learning and Artificial Intelligence with Industrial Applications (10 credits): This module will discuss the tools used in artificial intelligence and machine learning, as well as the advantages of employing such tools in Industrial operations. It will focus on real-world business applications, explaining the most common algorithms in a simple and straightforward manner. This module, rich in case studies, will give a working awareness of Machine Learning's current and future capabilities. This module will run over two semesters – Semester 2 & semester 3).
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Semester 3:
- ML and AI with Industrial Applications(10 Credits) (continued..).
- Applied Data Architecture (5 credits): This module focuses on the suitable, selection and integration of data storage and processing components. Students will connect to and utilise relational, graph, document and time series stores for various applied data scenarios. Benefits and issues associated with polyglot persistence will be explored, including synchronisation and two-phase commit. Integration patterns incorporating message brokers and event streaming will be introduced. Provisioning and scalability will be discussed.
- Applied Industry Project (10 credits): In this module learners apply knowledge gained on programme modules either in an industry setting or by completing an industry relevant project. Industry-relevant experience will be gained either in the student's current employment setting (if appropriate), through a work placement or via an applied industry relevant project (which may be individual or group). In the case of an employment or work-placement based scenario, learning will be demonstrated through an agreed portfolio of work or agreed work-based project, while in the case of an industry-relevant data-driven project through the project output (documentation and implementation). They will find appropriate data to investigate, formulate a research hypothesis, explore the data using visualisation techniques, undergo an appropriate analysis of the data and evaluate these results.
Education Progression
Fees and Funding
EU Fees: €9,000 or
(for those who meet Springboard HCI funding requirements fee): 0% up to 10%(€900).
International Fee: Please see International Fees
You may be eligible for financial support for this course through the Student Universal Support Ireland (SUSI) system. Find out more
Entry Requirements
This standard minimum entry requirements are a 2.2 Honours Degree (NFQ Level 8) or equivalent in STEM (Science, Technology, Engineering, and Mathematics), who have substantial technical and numeracy competencies. Graduates from non-STEM disciplines who have not developed these skills as part of their studies, will be required to demonstrate numeracy and problem-solving ability as well as an aptitude for technology.
How To Apply
Apply on Springboard Portal
Applications for this course are now being accepted through the Springboard portal.
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Disclaimer: All module titles are subject to change and for indicative purposes only. All courses are delivered subject to demand and timetables are subject to change. Elective Module options will only run subject to student numbers. The relevant Department will determine the viability of each elective module option proceeding depending on the number of students who choose that option. Students will be offered alternative elective modules on their programme should their preferred elective option not be proceeding. Award Options for Common Entry Programmes: The relevant Department will determine the viability of each award option proceeding depending on the number of students who choose either option. If the numbers for one of the Award options exceed available places, students for this option will be selected based on Academic Merit (highest grades).
