Data Analysis and Machine Learning

This module is designed to equip students with the necessary skills and knowledge required for data analytics. It covers both fundamental and advanced techniques needed for data analytics, including data management, processing, summarisation, and predictive analytics. Through this module, students will develop a strong theoretical foundation in data analysis and machine learning. They will also gain hands-on experience in applying these techniques to real-world problems. The module focuses on using the Python programming language as a practical tool for demonstrating various techniques.

Upon successful completion of this micro-credential students will be able to:

• Describe several widely used methods for data storage, including specialized file formats, SQL and NoSQL databases, and key-value stores.
• Explore datasets using summary statistics, statistical plots, and advanced data visualization methods (e.g. t-SNE)
• Describe supervised machine learning theory, including problem types, best practices for data preparation, model selection, overfitting and underfitting, and bias-variance tradeoff.
• Apply fundamental and advanced classification and regression algorithms including: linear and nonlinear regression, discriminant analysis, decision trees, logistic regression, support vector machines, and ensembles.
• Perform various types of generic unsupervised data analytics including cluster analysis, density estimation, and dimensionality reduction.
• Describe the principles of modern representation learning and deep learning techniques and evaluate the merits of several state-of-the-art models.
• Demonstrate a critical appreciation of available software packages for data analysis.
• Demonstrate the ability to implement a predictive analytics pipeline.

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
• Please upload a CV

If applicable, evidence of competence in the English language as per DCU entry requirements.  Please see here.