module specification

CC7183 - Data Analysis and Visualization (2024/25)

Module specification Module approved to run in 2024/25
Module title Data Analysis and Visualization
Module level Masters (07)
Credit rating for module 20
School School of Computing and Digital Media
Total study hours 200
 
100 hours Guided independent study
48 hours Scheduled learning & teaching activities
52 hours Assessment Preparation / Delivery
Assessment components
Type Weighting Qualifying mark Description
Coursework 60%   Coursework apply data analysis and visualisation techniques in a data analytical task (2500 words report + a data analys
Unseen Examination 40%   2-hour Unseen Exam
Running in 2024/25

(Please note that module timeslots are subject to change)
Period Campus Day Time Module Leader
Autumn semester North Thursday Afternoon

Module summary

This module explores fundamental concepts for analysing and visualising data. The module covers descriptive statistics for exploratory data analysis, correlation analysis and linear regression model.  Graph and text data analysing techniques for web and big data and reporting the results and presenting the data with visualisation techniques are also discussed. A substantial practical element is integrated into the module to enable students to apply data analysis and visualisation techniques for real world data analytical problems.

The aims of this module are to:
• enable students to gain understanding of fundamental concepts in data analysis and visualisation,
• develop students expertise in data analysis with descriptive statistics, correlation analysis,  and linear regression  model,
• enable students to acquire knowledge of graph and text data analysing, and
• develop students with practical skills in applying data analysis and  visualisation techniques for real world data analytical problems.

Syllabus

• Fundamental concepts in data analysis and visualisation (Theories and hypotheses, Population and samples, Relationships and causality, Data sets, Reliability and validity, basic visualisation techniques) [LO1, LO2, LO6]
• Numeric and categorical variable analysis and visualisation [LO3, LO4]
• Exploring data quality dimensions and relationships of variables with visualisation [LO5]
• Modelling relationships of multiple variables with linear regression [LO4]
• Graph and text data analysing and visualising techniques for web and big data [LO5]
• Visualization techniques for clustering techniques [LO2]
• Applying and Visualising Time Series Analysis [LO2]
• Reporting the results and presenting the data with visualisation techniques to range of stakeholders [LO3, LO6]

Balance of independent study and scheduled teaching activity

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

- Lecture (2 hour / week):
Introduction of the major topics identified in the syllabus, plus practical exercises, directed reading and other further studies

- Workshop (2 hour / week):
Practical skills will be further developed through lab-based workshops. Specific practical exercises are set to support students' development of skills with powerful interactive data analysis package.

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

Learning outcomes

[LO1]  Demonstrate a comprehensive understanding of the various fundamental concepts in data analysis and visualisation.
[LO2]  Understand the importance of Visualisation for Exploratory Data Analysis
[LO3]  Gain a considerable exposure to the practical issues, as well as their theoretical underpinning, pertinent to apply data analysis and visualisation techniques for real world data analytical problems independently with data analytical tools
[LO4]  Achieve and show sufficient level of knowledge and proficiency in data analysis with descriptive statistics, correlation analysis, and linear regression model.
[LO5]  Demonstrate an enhanced awareness of some current developments in graph and text data analysis.
[LO6]  Understand and participate in the legal, social, ethical and professional framework for the analysis and visualisation of data.

Bibliography

https://rl.talis.com/3/londonmet/lists/45CA1965-4AF0-AA6F-AD62-67E12BAE6818.html?embed=1

 

1. EMC Education Services (2015), Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, 1st Edition, Wiley. [CORE]

2. Pallant, Julie F. (2020) SPSS Survival Manual : a step by step guide to data analysis using IBM SPSS, 7th Edition, Allan & Unwin. 
3. Brian Everitt, Torsten Hothorn (2011) An introduction to applied multivariate analysis with R. Springer.
4. Sullivan, Dan (2019) Advanced SQL for Data Science : Time Series / with Dan Sullivan, Carpenteria.
5. Richard Brath, David Jonker (2015) Graph Analysis and Visualization. Wiley.
6. Stephen Few, (2009) Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press.