Data Analysis and Machine Learning I

Data Analysis and Machine Learning are central to solving complex problems in today’s world, from healthcare and finance to climate science and engineering. They enable us to uncover patterns in large datasets, make accurate predictions, and automate decision-making processes that were once manual and time-consuming. These skills are not only academically valuable but also highly practical, preparing students to apply advanced analytical techniques and machine learning models to real-world challenges, ensuring they are equipped for both research and industry demands in a data-driven era.

This module will provide students with fundamental and advanced skills required for data analysis, including: data management, processing, summarisation, visualisation, machine learning and predictive analytics. It is focused on providing students with a strong theoretical foundation, along with the ability to make practical use of the advanced techniques in the field. The Python programming language will be used for demonstrating the use of various techniques throughout the module, giving students practical tools for solving relatively sophisticated and broadly-defined real world problems in a well-established and widely-used programming environment.

On successful completion of this module, learners will be able to:

• Describe several widely used methods of data storage, including different data formats. Demonstrate an understanding of a variety of data management tools.
• Apply data pre-processing techniques, including data cleansing.
• Explore datasets and generate summary statistics across a range of datasets. Understand the benefits of summary statistics and carry out dataset analysis.
• Visualise dataset characteristics using a variety of graphs and employ advanced data visualisation techniques.
• Describe the principles of supervised machine learning, understand a range of supervised learning algorithms, and apply machine learning tasks to different datasets.
• Describe the principles of unsupervised machine learning, understand a range of unsupervised learning algorithms, and apply machine learning tasks to different datasets.

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.

Good understanding of mathematical concepts in Linear Algebra, Probability, and Calculus would be recommended, as well as strong programming skills, preferably in Python. 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.