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Data Analytics - Professional Diploma - UL@Work

Course Details


Available: Part- Time

Duration: 1 Year

Award: Professional Diploma

Qualification: NFQ Level 9 Major Award

Faculty: Science and Engineering

Course Type: Professional/Flexible

Fees: For Information on Fees, see section below.

Application Deadline: Saturday, December 31, 2022


Professor Norma Bargary
Department of Mathematics and Statistics
Sharon Barrett
Express Interest

Register your interest here
to be notified when applications are open.

Brief Description

Next Intake: January 2023

The UL @ Work Professional Diploma in Data Analytics online will provide an exciting opportunity for work-based learners to upskill in the rapidly growing area of data analytics. This part-time postgraduate course in data analytics will provide professional learners with a fundamental grounding in the key skills of data analytics including data interrogation, visualisation and predictive analytics and how to use these to obtain valuable insight from big datasets. This level 9 professional diploma counts as 30 credits toward a Masters in Digital Futures.

No prior programming experience is necessary to succeed on this programme.

Download Course Information Sheet Here


1 An understanding of how data-driven models can improve your ability to make decisions in a fast-paced and uncertain world, and the ability to use modelling to predict future trends.

2 R-Shiny data visualisations, dashboarding and reporting skills, with which to clearly communicate your findings and business needs.

3 Real life examples as proof of your ability to analyse, summarise, visualise, and report on insights extracted from a dataset.


Next Intake: January 2023

The programme will be delivered fully online. Students will experience a supportive and dynamic learning environment in which there will be an emphasis on peer learning, team learning, critical reflection and feedback. 

Between session students will be supported by faculty and staff online and will be required to carry out between 15 and 18 hours of study per week.  

Semester 1 ECTS Credits Semester 2 ECTS Credits
Introduction to predictive analytics 6 Statistical learning with applications 6
Data analytics with R 6 Advanced predictive analytics 6
Future focused professional portfolio 1 3 Future focused professional porfolio 2  3

On successful completion of this programme, the graduate will be able to:

  • Demonstrate an understanding of the role of statistics and data analytics in industry.
  • Demonstrate in-depth knowledge and understanding of the key concepts and methodologies underpinning data analytics, and the skills required by a practising data scientist within a complex and evolving work environment.
  • Develop a mastery of key statistics and data analytics methods. Apply these methods to a range of real-world problems, demonstrating an awareness of each method's advantages and disadvantages.
  • Approach problems in a precise and rigorous way, with the ability to solve industry problems and make effective decisions.
  • Analyse and interpret data, synthesising large amounts of information to draw appropriate conclusions.
  • Demonstrate exceptional communication skills (both written and oral) and the ability to present technical content in a clear and precise way suitable for a range of stakeholders.
  • Form and work in multidisciplinary teams in response to data analytics challenges in the workplace.
  • Identify the appropriate data analytics method(s) to implement for a variety of scenarios.
  • Investigate the strengths and limitations of a chosen approach for a given situation and research alternatives as appropriate.
  • Demonstrate personal effectiveness skills such as critical thinking, problem-solving and communication.
  • Reflect on and identify the contribution and perspectives of individual disciplines in addressing complex, trans-disciplinary problems.
  • Appreciate the complexity and nuances of statistics and data analytics methods and their application to real-world problems.
  • Demonstrate an understanding of the importance of the data collection and presentation process, and how to draw appropriate conclusions.
  • Demonstrate ethical reasoning skills and understand their professional responsibilities when using data for decision-making.
  • Reflect on individual roles within an interdisciplinary process and workplace.


Applicants are normally expected to hold a primary honours degree in a cognate discipline, (minimum H2.2), or equivalent and have at least 5 years of relevant industrial experience. 

Candidates who do not meet the minimum entry criteria may be considered in accordance with the University's policy on the Recognition of Prior Learning.

These candidates will be required to submit a portfolio to demonstrate their technical and/or management experience.

Alternative Entry Route applicants will be required to undertake an interview and satisfy the course admission team that they have the experience, motivation and ability to complete and benefit from this course.

What to Include with your Application

  1. Delays in receiving these documents will affect the progress of your application.
  2. Photo or Scanned original copy of your transcripts for all years of study. (Graduates of UL need only provide us with their Student ID number.)
  3. Photo or Scanned copy of passport to verify ID and full legal name.
  4.  A copy of your most recent CV.
  • If your qualifications have been obtained in a country where English is an official language this will suffice
  • If this is not available, the following additional documents must be provided:
    • English translation of your qualification(s)/transcripts
    • English language competency certificate

    For more information Click Here



EU:€3,500, Non-EU: €4,750 

For information on funding and scholarships please click here.

“Learnings are immediately applicable from the business perspective & have an impact on our ability to deliver high value solutions to customers across the wide range of industries & markets we support.”

Rosemary Ryan, Analog Devices

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