new course

Postgraduate Diploma in Science in Data Analytics (Full-Time)

Course Points
Course Duration
1 Year
Course Level
Level 9
Course Start Date
28th September

Course Summary

The Postgraduate Diploma in Data Analytics is a new offering in this area and will be delivered as a one-year, full-time, level 9 programme and funded via Springboard+ (Human Capital Initiative Pillar 1). It is primarily aimed at highly numerate level 8 graduates who wish to extend their skillset to a more advanced level of theoretical knowledge and practice, and to further develop their analytic capabilities and innovation skills in the area of Data Analytics. The programme takes an integrative approach, focusing on the synthesis of knowledge and practice from specialist areas within the data analytics and computing domains. The primary aim is to develop expert knowledge in aspects of data required to become highly-skilled Data Analysts: Statistics, Data Visualisation, Time Series, Data Architecture, Machine Learning, Statistical Programming. The programme culminates in a 15 credit data project module, through which knowledge gained across modules will be applied to a data-driven, industry related project. The programme culminates in a 10 credit data project module

Important notice about this course


  • This full-time 1-year postgraduate programmes is funded via Springboard+

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.

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.


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.

This course is typically delivered using a blended approach and will be delivered in full-time mode

  • 16 Weekly Contact Hours (3 days a week)
  • 24 Weekly hours - Directed/Independent Study

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.

The following are eligible to apply for courses in 2020/21 academic year (subject to the applicant meeting all requirements, e.g. academic requirements):

  • Returners: May apply to all courses if they meet the nationality/visa requirement and residency criteria.
  • People in employment: May apply to all courses if they meet the nationality/visa requirement and residency criteria.
  • The unemployed or formerly self-employed: All courses are open to these categories of applicants
  • Recent Graduates: One-year full-time and two-year part-time ICT Skills Conversion courses are open to recent graduates. However, to participate in an NFQ Level 9 (Post-Graduate) course, 2020 graduates will be required to pay 10% of the course fee (€785.70).

For more information of fees and eligibility criteria, please visit the Springboard+ Website. You may also contact Dr Tim McCormac for more information about Fees for this programme:

DkIT Graduate Office
Unit 13, Regional Development Centre


T. +353 (0)429370458

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.

Graduates with strong numerate skills who wish to pursue a career in the expanding area of Data Analytics.

Professionals who wish to develop their Data Analytics and Mining skills to apply them to real problems in their current work domains.

Graduates who enjoy Mathematics and problem-solving with a focus on real-world problems.

Graduates who are interested in developing their advanced statistical and computing skillsets to master areas such as statistical inference and machine learning.

Dr Fiona Lawless (Head of Computing Science and Mathematics)
Phone: +353 42 9370200

Dr Rajesh Jaiswal (Programme Director)
Phone: +353 (0)429270200

Dr Tim McCormac (Head of Research & Graduate Studies)
Phone: +353 (0)429370458

Course ID N/A
Course Type Springboard, Postgraduate
Study Mode Full-Time
Level 9
Duration 1 Year
Fees €785.70
Starting Date 28th September
School School of Informatics & Creative Arts
Department Computing Science and Mathematics
Credits 60
Awarding Body Dundalk Institute of Technology
Apply to Dundalk Institute of Technology

How to Apply

You can enrol online for this course today via Springboard+ by clicking the button below and following the instructions to apply.


For further enquiries, please contact