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Course Details
Course Code(s):
MS6062
Available:
Part-Time
Intake:
Course Start Date:
Autumn 2025
Duration:
6 Weeks
Award:
University Certificate of Study
Qualification:
NFQ Level 9 Minor Award
Faculty:
Science and Engineering
Course Type:
Professional/Flexible, Online
Fees:
For Information on Fees, see section below.
Application Deadline:
Contact(s):
for more information or to be notified when applications are open.
Brief Description
This module will cover an introduction to programming in R and RStudio for professionals working in the wind energy sector, assuming no prior coding experience.
FUNDING
Funded places are offered on a first-come first-served basis, but these are limited and are strictly subject to meeting Springboard+ eligibility. Once the funded places have been filled, the course may remain open for those who wish to apply for a self-financed place.
APPLICATION INFORMATION
Please ensure you enter the Module Code when applying for this module. Applications without this cannot be processed. You may apply for more than one module under the same application.
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The module will cover data wrangling, data visualisation, summarising data, and simple statistical modelling in R. Students will learn about reproducible research, coding and analytics practices, and be given an introductory overview of RShiny dashboards for data generated in wind energy applications.
On successful completion of this module, students will be able to:
- Demonstrate proficiency in standard programming constructs in R and RStudio.
- Examine and evaluate a dataset through visualisation methods.
- Summarise data using appropriate summary statistics.
- Develop a pipeline to visualise, summarise, and model data.
- Apply reproducible coding practices to wind energy datasets.
- Develop interactive visualisation tools for dissemination.Synthesise information across the data analytics pipeline for decision-making.
- Formulate a well-constructed rationale to defend and justify the analytics approaches adopted.
- Display a professional commitment to reproducible data analytics practices.
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.
Entry requirements are established to ensure the learner can engage with the course material and assessments, at a level suitable to their needs, and the academic requirements of the module. By applying to this micro-credential, you are confirming that you have reviewed and understand any such requirements, and that you meet the eligibility criteria for admission.
Successful completion of this module does not automatically qualify you for entry into a further award. All programme applicants must meet the entry requirements listed if applying for a further award.
The fees for this module are €1,250
Springboard - Candidates who satisfy the eligibility criteria under Springboard+ can qualify for 50% funding subject to the availability of places. To clarify eligibility please go to
Graduate and Professional Studies
+353 (0)61 234377
ºÚÁÏÉç, Limerick, Ireland