module specification

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.

• 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]

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.

[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.

The module will be assessed by a practical piece of coursework (60%) and a 2-hour unseen examination (40%).

The coursework is designed mainly to assess the practical aspects of the module. It will provide students with the opportunity to undertake research on current issues and practical techniques in data analysis and visualisation and its effective application for real world data analysis problem [LO1, LO2, LO3 and LO5]. It will also enable students to apply their knowledge to a practical business problem, demonstrating their skills for problem-solving and critical thinking/evaluation [LO2, LO5].

The unseen examination will provide an opportunity for students to demonstrate their understanding of data analysis concepts and techniques and their ability to apply these techniques appropriately to the solution of given problems/scenarios [LO1, LO3, LO4]. The examination will test the students' retention, understanding and insight of material drawn from the module.

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.