Data Analysis & Machine Learning II

Data Analysis and Machine Learning I and II, offered by the School of Electronic Engineering at Dublin City University. The first course introduces undergraduate students to Python-based data processing, focusing on fundamental skills such as data cleansing, statistical summarisation, and basic predictive modelling. In contrast, the second module is a more advanced postgraduate-level course that covers complex topics like deep learning architectures, neural networks, and the theoretical trade-offs of model selection. Both modules share a similar structure, requiring a total of 188 work hours and assessing students through a combination of a practical assignment and a final written examination. Ultimately, these sources provide a comprehensive roadmap for mastering modern data analytics and sophisticated machine learning pipelines using industry-standard software tools.

The learning outcomes of Data Analysis and Machine Learning I and Data Analysis & Machine Learning II reflect a progression from foundational data wrangling and general machine learning to advanced theory, deep learning, and end-to-end pipeline implementation.

Here is how their learning outcomes differ across key areas:

1. Data Wrangling, Exploration, and Visualisation

• Module I: Focuses heavily on the full data preparation lifecycle. Its outcomes explicitly require students to describe data storage formats and tools, perform data cleansing/pre-processing, generate summary statistics, and apply data visualisation techniques.
• Module II: Does not have dedicated outcomes for data storage, summary statistics, or visualization graphs. Instead, data preparation is embedded within broader machine learning theory and pipeline development.

2. Machine Learning Theory and Advanced Algorithms

• Module I: Focuses on describing and applying standard supervised and unsupervised machine learning algorithms to datasets.
• Module II: Moves into deeper theoretical concepts, requiring students to describe concepts such as the bias-variance tradeoff, overfitting and underfitting, model selection, and problem types. It also requires applying both fundamental and advanced classification and regression algorithms.

3. Deep Learning and Representation Learning

• Module I: Does not explicitly cover deep learning or neural networks in its learning outcomes.
• Module II: Explicitly includes outcomes dedicated to modern representation learning, evaluating state-of-the-art models, and applying deep learning techniques using artificial neural network architectures.

4. Pipelines and Software Appreciation

• Module I: Emphasises learning data management tools and applying algorithms to various datasets.
• Module II: Requires students to implement a complete predictive analytics pipeline and demonstrate a critical appreciation of available software packages for data analysis.

A Primary Honours degree, Level 8 in Electronic/Electrical/Computer Engineering, Applied Physics, Computer Sciences or other Cognate/Engineering Disciplines. This module is the follow-on module for the Data Analysis & Machine Learning I module, applicants should have successfully completed this module before attempting Data Analysis & Machine Learning II. 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.

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. (https://hea.ie/skills-engagement/hci-pillar-3-micro-credentials-learner-fee-subsidy/).