Data at Speed and Scale

This module will equip students with practical knowledge of how to use suitable technologies for large-scale data processing, including batch and stream processing. The module introduces the theory and practice of massively parallel data processing, leveraging different hardware and software infrastructures, including cloud-based infrastructures. It includes a practical component with development of Big Data analytics on suitable publicly-available test data using high-level languages and suitable libraries.

Upon completion of the module, students will be able to:

• Understand the nature and consequences of Big Data for processing and analytics.
• Design and Implement data-intensive applications using existing best-of-breed big data libraries and frameworks.
• Discuss the role of cloud services in the design of big data systems.
• Apply machine learning techniques to Big Data.
• Explore and curate large, complex datasets for use in analytics.
• Configure and deploy data analytics pipeline.
• Understand and discuss some of the design considerations for high-performance analytics.

A Primary Honours degree, Level 8 in Electronic/Electrical/Computer Engineering, Applied Physics, Computer Sciences or other Cognate/Engineering Disciplines. Applications are also invited from diverse educational and/or employment backgrounds, with applications evaluated on a case-by-case basis.  

And also to indicate the required documentation:

• Please provide Academic Transcripts for final year of study where appropriate (English translation)
• All applicants must submit a copy of their passport

There is no availability for a deferred entry onto a micro-credential.

We would suggest that students revise their competences in SQL query, Java/Python programming, command-line interfaces and unix commands. If applicable, evidence of competence in the English language as per DCU entry requirements. Please see here.

For further information regarding the HCI learner subsidy eligibility criteria please click here.