Big Data Analytics (Distance Learning) - CMS7504

Assessment 1: Other

Students will undertake a series of formative exercises through the University’s Virtual Learning Environment. Feedback, advice and guidance will be provided for these exercises.

Other

Assessment 2: Project Work

This project work usually consists of the design, implementation and evaluation of a big data analytic pipeline for a specific large dataset.

Project Work

Assessment 1: Written Assignment

This written assignment consists of an essay evaluating the state-of-the-art in a focused area closely related to big data analytics. Word count is indicative and does not include table of contents, tables, figures, diagrams, references and appendices.

Written Assignment

<p>The ever-increasing advancements in sensing technologies, network infrastructure, storage and social media have enabled us to acquire an unprecedented volume of data at an explosive rate. As a result, the ability to efficiently and accurately derive human-understandable knowledge from these datasets has become increasingly critical to our digitally-driven society and economy. Under this Big Data phenomenon, tremendous endeavours have been devoted to tackle its underlying challenges through both novel solutions and the evolution of existing methodology. The module aims to provide you with the knowledge and critical understanding of contemporary challenges posed by the big data. The topics covered here include the fundamental characteristics and operations associated with big data; existing and emerging architectures and processing techniques; domain applications of big data in practice. Through this module, you will develop an informed understanding of the principles and practice of big data analytics in both general and application specific contexts.</p>

School of Computing and Engineering

<p>https://library.hud.ac.uk/readinglists/CMS7504</p>

Computing and Information Systems

CMS7504 - Big Data Analytics (Distance Learning)

Big Data Analytics (Distance Learning)

Critically evaluate robust architectural and functional designs for the processing of big data, according to established benchmarks and practices.

Critically evaluate existing and emerging algorithms and methods for analysing complex and high-volume datasets.

Demonstrate the application of method and design through the construction of a fully functioning prototype incorporating existing and emerging technologies.

Select, apply and appraise appropriate techniques for handling and analysing big data.

Rationalise the fundamental principles of big data, and the associated challenges in processing and analyses.

Apply the principles of contemporary architecture and design for big data analytics.