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Module: Archived Version 2023 - 2024
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Archived Version 2023 - 2024

Module Title
Module Code
School

Online Module Resources

NFQ level 8 Credit Rating 7.5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Description

This module addresses the three Vs of Big Data: Volume, Velocity and Variety. This module will equip students with detailed knowledge of mining massive data sets, processing streams of data in real-time, and extracting knowledge from complex information. The module introduces the theory and practice of massively parallel data processing, leveraging different hardware and software infrastructures, including could-based infrastructures. It includes a practical component with development of Big Data analytics on suitable publicly-available test data using high-level languages and suitable libraries.

Learning Outcomes

1. Understand the nature and consequences of Big Data for processing and analytics
2. Design and Implement data-intensive applications using existing best-of-breed big data libraries and frameworks
3. Discuss the role of cloud services in the design of big data systems
4. Apply machine learning techniques to Big Data
5. Explore and curate large, complex datasets for use in analytics
6. Configure and deploy data analytics infrastructure
7. Understand and discuss some of the design considerations for high-performance analytics
8. Gain detailed knowledge of map-reduce, related distributed file systems and their open-source implementations



Workload Full-time hours per semester
Type Hours Description
Lecture36Lectures and tutorials presenting the key theoretical aspects of the course. Lecture material will be provided in the form of online notes, research papers, technical documentation and multimedia content as applicable.
Laboratory24Hands-on Programming laboratory work and tutorials incorporating problem-based learning tasks, formative assessments, and student-led discussions. This will include significant technical work to configure, deploy, program and execute data analysis software.
Independent Study190Significant individual work including reading and understanding technical papers, research material, documentation. Preparation of continuous assessment, discussion of coursework with peers and group assignment.
Total Workload: 250

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

Big Data Processing and Mapreduce
Introduction to Big Data, and why Big Data analytics is different to conventional approaches. Introduction to the Mapreduce algorithm and its open-source implementation. The Hadoop ecosystem and how it can be used to analyse data.

Finding Similar Items
Theoretical topics include Locally-sensitive Hashing, Minhashing, Similarity-preserving summaries, Distance measures. These form the basis for organising and exploring Big Data.

Stream Processing
Handling real-time / stream data through the use of Filtering, Sampling, Estimation of Moments, and other techniques. Practical aspects include programming with Spark, Storm or a similar library.

Large-scale machine learning
Key topics include Item Similarity, Clustering, and evaluating performance.

Big Data Cloud
Configuring and using Amazon EC2, Elastic Mapreduce, Microsoft Azure and similar technologies. The students will deploy applications to these platforms as part of their assignments.

Assessment Breakdown
Continuous Assessment% Examination Weight%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Reassessment Requirement
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
Unavailable
Indicative Reading List

    Other Resources

    None
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