module specification

This module provides an understanding of big data processing and visualisation approaches and challenges along with various techniques and technologies. It covers big data processing and basic visualisation concepts, different type of big data and real-time log analysis using charts, graphs, diagrams of 2D data. A substantial practical element is integrated into the module to enable students to apply big data processing, querying and visualisation techniques for real-world problems using cloud and desktop technologies.

The module aims are to:

• Enable students to gain further understanding of data streaming, processing and querying.
• Enable students to gain understanding of the fundamental concepts of data visualisation.
• Develop students’ practical skills in applying data processing and visualisation techniques for real world big data problems.
• Expose student’s expertise in data model visualisation techniques of different types of data and types of tools and their methods.

Data Engineering

A brief outline of the indicative syllabus in narrative form identifying key subject areas to be addressed in discrete elements of the course:

1. Big data processing and querying (e.g. DataFrames, SQL, and Datasets, Spark’s APIs, Application Production) LO1

2. Real-time data streaming (e.g. Understand publish-subscribe messaging and how it fits in with big data, writing messages and reading data with Kafka) LO2

3. Introduction to big data visualisation, approaches, challenges, philosophies, quality and reporting the results LO3

4. R visualisation - Decision Trees, Naive Bayes, Classification LO4, LO5

5. Tableau visualisation - charts, graphs, diagrams with sales data LO4, LO5

6. D3 visualisation - web charts, graphs, diagram LO4, LO5

Topics will be introduced through the medium of formal lectures, supported by tutorial and workshop sessions, and blended learning as follows:

- Lecture (1 hour / week):
Introduction of the major topics identified in the syllabus, plus practical exercises, directed reading and other further studies.

- Workshop (2 hour / week):
Data Visualisation technical skills will be further developed through lab-based workshops. Specific practical exercises are set to support students with visualisation skills with relevant packages.

- Blended learning:
Using the University’s VLE and online tools to provide deliver content, assessment and feedback, to encourage active learning, and to enhance student engagement and learning experience.

Students will be expected and encouraged to produce reflective commentaries on the learning activities and tasks that they carry out to complete their work.

• [LO1] Enhance the understanding of concepts and methods of big data processing and querying, and practice to solve real-world problems with a big data framework.

• [LO2] Demonstrate the process of big data streaming and batch processing to solve high volume real-world problems, based upon the best suited technique.

• [LO3] Demonstrate a sound understanding of concepts, approaches and challenges to visualise big data in effective and efficient ways.

• [LO4] Present sufficient level of proficiency in visualising big data using various technologies to address the challenges.

• [LO5] Demonstrate the process of big data visualisation to solve real-world problems, based upon the best suited techniques.

Link can be found at: http://https://rl.talis.com/3/londonmet/lists/309423DF-4EEB-1005-3261-B72A80164C37.html?draft=1&lang=en-GB&login=1