Natural Language Processing

Key programme benefits to future students

This programme has been developed for working professionals and is designed to be flexible and accessible. You can take it online, at your own pace, and from anywhere worldwide.



With the growing amount of data in digital form, skills in NLP have become crucial for businesses to extract insights from written and spoken human language.



The Professional Diploma in Natural Language Processing is for those who want to deep dive into this specialist field for the expansive career opportunities it presents



Key Fact: Build Your Master Plan and Stack Your Way to Success. You can take this Professional Diploma as the final 30 ECTS Credits required to stack into your Master of Professional Practice (MPP).

Autumn Modules

• Introduction to Natural Language Processing

• Future Focused Professional Portfolio 1

• Information Retrieval



Spring Modules

• Advanced Natural Language Processing

• Natural Language Understanding

• Future Focused Professional Portfolio 1

The principal entry requirement is a Level 8 honours degree, at minimum second class honours (NFQ or other internationally recognised equivalent), in a relevant engineering, computing, mathematics, science or technology discipline.



Alternative Entry Route:

In accordance with the University's policy on the Recognition of Prior Learning, candidates who do not meet the minimum entry criteria may be considered. Applicants from other disciplines who have a relevant mathematics and computing element in their primary degree will also be considered. Applicants who possess an honours u/g degree, minimum second-class, or equivalent in a non-numerate discipline and have a minimum of 3 years experiential learning in an appropriate computing discipline will be considered. English Language Requirements, right to shortlist, interview, and RPL policy will apply.

1 year part-time, online.

Autumn

Graduate careers

The skills you’ll acquire from this Professional Diploma in NLP are transferable to various job roles. For example: Data Scientist - In this role, you’ll use NLP to extract insights from text data and build predictive models. You’ll work with large datasets and use statistical methods to find patterns in the data. Machine Learning Engineer - As a machine learning engineer, you’ll work on developing algorithms that enable machines to learn from text data. You’ll collaborate with data scientists and software engineers to build and deploy NLP models. Computational Linguist - In this role, you’ll work on developing and improving NLP algorithms. You’ll use your understanding of human language to improve the accuracy of NLP models and make them more efficient.