Module Specifications..
Current Academic Year 2023  2024
Please note that this information is subject to change.
 
Repeat examination 

Description This module is accredited by NUIG. Largescale data analytics is concerned with the processing and analysis of large quantities of data, typically from distributed sources (such as data streams on the internet). This module introduces students to stateoftheart approaches to largescale data analytics. Students learn about foundational concepts, software tools and advanced programming techniques for the scalable storage, processing and predictive analysis of high volume and highvelocity data, and how to apply them to practical problems. ** This module uses Java as programming language. Knowledge of Java is a prerequisite for participation in this module. ** Planned topics include: Definition of largescale computational data analytics; Overview of approaches to the processing and analysis of high volume and high velocity data from distributed sources; Applications of largescale data analytics; Foundations of cluster computing and parallel data processing; The Hadoop and Spark ecosystems. MapReduce; Advanced programming concepts for largescale data analytics; Concepts and tools for largescale data storage; Stream data analytics. Complex Event Processing (CEP); Techniques and opensource tools for largescale predictive analytics; Computational statistics and machine learning with largescale data processing frameworks such as Spark; Privacy in the context of largescale data analytics. Further information pertaining to the module is available from NUIG.  
Learning Outcomes 1. Be able to define largescale data analytics and understand its characteristics 2. Be able to explain and apply concepts and tools for distributed and parallel processing of largescale data 3. Know how to explain and apply concepts and tools for highly scalable collection, querying, filtering, sorting and synthesizing of data 4. Know how to describe and apply selected statistical and machine learning techniques and tools for the analysis of largescale data 5. Know how to explain and apply approaches to stream data analytics and complex event processing 6. Understand and be able to discuss privacy issues in connection with largescale data analytics  
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 

Indicative Content and Learning Activities
 
 
Indicative Reading List  
Other Resources None  
Programme or List of Programmes
 
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