Postgraduate Diploma in Big Data Management and Analytics

This module aims to introduce the learner to the fundamental principles of data science and equips them with “data-analytic thinking” necessary for extracting useful knowledge and business value from the relevant datasets. The module introduces the learner to the principles underpinning the processes and strategies necessary to solve real-world problems through data science techniques. The module focuses on data science concepts as applied to practical real-world problems and aims to teach learners the underlying concepts behind data science and most importantly how to approach and be successful at problem-solving. Problem-solving and information discovery strategies will be developed via in-depth analysis of existing Big Data implementations and case studies. As most of the information discovered from large datasets is of direct use to business decisions, both reporting and visualization are an important element of this module.  

​This module aims to equip the learner with a range of most relevant topics that pertain to contemporary analysis practices, and are foundational to the emerging field of big data analytics. Learners are guided through the theoretical and practical differences between traditional datasets and Big Data datasets. An overview of the initial collection of data will be explored for multiple data sources. A formal grounding in analytical statistics is a major part of the module curriculum. Learners are expected to apply principles of statistical analytics to solve problems and inform decision making. Learners achieve this through developing knowledge and understanding of statistical analytics techniques and principles while applying these techniques and principles in typical real world scenarios. 

This module serves to significantly deepen the learner's research skills, both in relation to the module related assignments and later in the completion of a dissertation/dissertation by practice. Specifically, it extends the ability of self-directed learners by equipping them with the appropriate vocabulary for reflecting on, critiquing and evaluating their own work and that of others. Throughout the module, learners are required to engage in a number of research methodologies and current research issues and trends in computing science. The module also addresses the need for good project management skills and techniques for the successful delivery of any project.

This module covers the fundamentals of artificial neural networks before exploring advanced architectures such as CNNs, RNNs, LSTMs, transformers, and large language models. Learners study autoencoders, generative models, and reinforcement learning techniques like Q-learning, with a focus on practical implementation, model evaluation, and hands-on exercises. Recent developments in deep learning and generative AI are also examined through analysis of cutting-edge research.

This module provides a comprehensive understanding of concurrent and parallel programming, from multicore and multiprocessor architectures to shared and distributed memory models. Learners explore threading, performance optimisation, debugging, and tools like OpenMP, as well as the Lambda Architecture for batch processing and real-time data streaming. Through case studies and practical activities, they develop the skills to address modern computational challenges.

This module introduces cloud computing infrastructure, examining leading solutions from major providers alongside ethical and research topics such as energy optimisation and standardisation. Learners develop applications for cloud platforms, compare them with traditional computing models, and gain insight into the future of cloud-based services.

This module aims to equip the learner with the skills to implement, from the batch to the speed layer, an end-to-end Big Data storage system using the most current technologies. As a grounding to the subject area, the learner will be guided through an overview of the traditional approach of data storage and access, with all theory grounded in real-world technological examples. As technologies have progressed, the availability of data has increased dramatically. The volumes of data dealt with in modern systems are far beyond what traditional systems can handle. During this module, the main failure points of traditional systems with regard to this level of data will be explored. Each layer of the Lambda Architecture will be explored in detail from theory through to implementation via current technologies. At the lowest layer, the module will demonstrate how to store Big Data in the fact-based model in a distributed file system, namely Hadoop Distributed File System (HDFS). This layer is then connected to a read-oriented database, such as MongoDB or ElephantDB, depending on the data type stored, to create the Serving Layer of the Lambda Architecture. Finally, this will be connected to a light-weight database that can handle high-volume reads and writes to implement the high-level Speed Layer of the Lambda Architecture. All practical work will be done on real-world data to emphasise the need for Big Data systems.

This module covers the principles and practices of data mining, from preprocessing and feature engineering to advanced tasks like classification, clustering, association rule mining, and anomaly detection. Learners gain hands-on experience with popular tools, evaluate model performance, and address ethical considerations such as fairness, privacy, and responsible data use. Recent advancements and real-world applications are explored through practical exercises and analysis of cutting-edge research.

Study Mode: Full-Time

Dublin: EUR 6,550.00

Study Mode: Part-Time

Dublin: EUR 6,550.00