Latest Module Specifications
Current Academic Year 2025 - 2026
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Description Today's business environment is characterised by enormous amounts of data from different sources and in different formats. This data has the potential to provide valuable information, leading to better decision-making and competitive advantage. However, managing, analysing and communicating the value of such a large amount of information requires specific skills. This module builds on the first-semester modules, BAA1088 and BAA1089, and provides students with a variety of advanced data analytics and statistical techniques to enable and support the analysis of such a vast amount of data. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Evaluate, select and apply appropriate project management methodologies (e.g. PRINCE2) and techniques to conduct a data analytics project (e.g. CRISP-DM). 2. Explain the role of advanced analytics and Big Data (including Hadoop) and evaluate how to adopt these approaches in different business environments. 3. Apply data mining techniques to pursue business objectives and follow best practices across different business environments. 4. Apply appropriate techniques to collect, clean, and prepare unstructured data for analysis. 5. Describe common unsupervised learning methods. 6. Explain the different Artificial Intelligence technologies, such as Natural language processing (NLP), Bidirectional Encoder Representations from Transformers (BERT), Deep learning, Generative AI/LLMs and evaluate the potential business implications of adopting such techniques in different business environments. 7. Critically analyse the outputs of the analysis to support and improve the decision-making process. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and Learning Activities
Introduction to Project Management Key concepts in project management including starting and initiating a project, controlling a stage, managing product delivery, managing a stage boundary, directing and closing a project. Managing data analytics projects Deploying a data analytics management framework (CRISP-DM, Data Lifecycle Management). Big Data Exploration of Data Mining (pattern discovery and predictions) and Data Analytics (analysis and decision-making) in the context of Big Data, using Hadoop. Unsupervised Machine Learning Concepts of dimensionality Reduction Techniques (Principal component analysis, Fisher linear discriminant analysis, t-Distributed Stochastic Neighbor Embedding) and clustering analysis (Hierarchical and partitional clustering). Text Analysis and Bidirectional Encoder Representations from Transformers (BERT) Data collections and preparation, information retrieval models, and Natural Language Processing. Pre-training and fine-tuning for NLP tasks (e.g., text classification, question answering). Contextual embeddings and self-attention mechanisms. Deep Learning Neural networks, activation functions, loss functions, and backpropagation. Types of architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. Deep Reinforcement Learning. Applications: Image recognition, sequence prediction, and feature extraction. Generative AI and Large Language Models (LLMs) Key concepts of Generative AI and the architecture of LLMs (e.g., GPT, BERT variants). Pre-training, fine-tuning, and transfer learning. Applications: Text generation, summarization, machine translation, and conversational AI. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List Books:
Articles:
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Other Resources
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