Postgraduate Diploma in Science in Data Analytics
Course Overview
The aim of this programme is to produce highly-skilled graduates with expertise that cuts across the core disciplines of Mathematics, Statistics and Computer Science. It emphasises the critical connection from data to information, from information to knowledge, and from knowledge to decision making, encompassed in the Data-Lifecycle.
The primary aim is to develop expert knowledge in aspects of data required to become highly-skilled Data Analysts: with modules in Statistics, Data Visualisation, Time Series, Data Architecture, Machine Learning, Statistical Programming and Ethics in Data.
The focus of this programme is the development of expert knowledge and highly sought after skills in the data analytics field and the application of that knowledge to industry-focused problems. However, the inclusion of cross-modular data projects together with a major 10-credit Data Project module gives this programme a very practical focus, enabling participant to apply the knowledge and techniques gained to industry-type problems.
Career Opportunities
The Post-Graduate Diploma in Data Analytics aims to address the urgent skills demands - it builds digital skills, develops critical thinking, analytical skills and business acumen in an area highlighted as crucial for the workplace of the future,
“Many of the fastest growing occupations and emerging industries require numeracy, knowledge of scientific and mathematical principles, as well as the ability to generate, understand and analyse empirical data and solve complex problems (UKCES, 2011). These skills make technological breakthroughs possible.”
It will address important issues in various modern Data Infrastructures through Machine learning and is well suited to data enthusiasts who are looking for opportunities across big data industries.
Graduates from the programme will be capable of assisting employers by providing analytical solutions that can give invaluable insights and trends in both public and private business sectors. The proposed programme has been specifically designed to provide local workers with upskilling opportunities, providing local companies with additional appropriately skilled graduates while at the same time improving the employability and mobility of individuals.
The focus on building expert knowledge and analytical skills and applying this to real- industry focused projects – from inception through all aspects of the data-life cycle - means students develop independent thinking and desired work-ready skills.
Course Delivery and Modules
This course is typically delivered on-campus and will be delivered in full-time mode
- 16 Weekly Contact Hours (3 days a week)
- 24 Weekly hours - Directed/Independent Study
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Semester 1
- Data Architecture (10 credits): This module runs for two semesters and provides a programming framework that would assist in solving big data problems in a distributed computing environment.
- Research Process for Data Analytic (5 credits)focuses on developing good research practice and prepare students for the research process required for the dissertation.
- Statistics (10 credits)is to build on the fundamental of mathematics and statistics needed for the masters whilst learning how to begin to apply these techniques to real data.
- Programming for Data Analytics (10 credits)introduces the programming tools in Python for Data Analysis that would assist students in subsequent modules of this programme.
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Semester 2
- Time series Analysis (5 credits)focuses on the analysis of data taken at regular time intervals, with real data applications in topics such as economics and finance.
- Machine Learning (10 credits)provides the knowledge and the applications of ML algorithms including Neural Networks.
- Applied Data Analytics Project (10 credits)Undertake an industry related data-driven project: manage lifecycle stages, ethical and GDPR issues.
Fees and Funding
EU Fees:
- Full-time (EU): tbc
- Part-time (EU): tbc
Non-EU Fees:
- Full-time (EU): tbc
- Part-time (EU): tbc
You may be eligible for financial support for this course through the Student Universal Support Ireland (SUSI) system. Find out more
Entry Requirements
The standard minimum entry requirement is a 2.2 Honours Degree (NFQ Level 8) or equivalent in the area of Mathematics, Statistics, Computing, Engineering, Science or Technology or equivalent qualification in a cognate discipline, which provided a strong foundation in Mathematics/Statistics and computer applications.
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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).
