module specification

CC7183 - Data Analysis and Visualization (2017/18)

Module specification Module approved to run in 2017/18
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
 
152 hours Guided independent study
48 hours Scheduled learning & teaching activities
Assessment components
Type Weighting Qualifying mark Description
Coursework 50%   Coursework apply data analysis and visualisation techniques in a data analytical task (2500 words report + a data analys
Unseen Examination 50%   2-hour Unseen Exam
Running in 2017/18
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.

Module aims

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)
  • Numeric and categorical variable analysis and visualisation with descriptive statistics
  • Examining relationships between interval data with correlation analysis
  • Modelling relationships of multiple variables with linear regression
  • Graph and text data analysing and visualising techniques for web and big data
  • Visualization techniques for interactive quantitative analysis of relationships and information
  • Reporting the results and presenting the data with visualisation techniques

Learning and teaching

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 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.

Learning outcomes

On successful completion of this module the student should be able to:
[LO1]  Develop a clear understanding of the various fundamental concepts in data analysis and visualisation.
[LO2]  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 with data analytical tools (e.g. SPSS, Tableau)
[LO3]  Achieve and show sufficient level of knowledge and proficiency in data analysis with descriptive statistics, correlation analysis, and linear regression model.
[LO4]  Demonstrate an enhanced awareness of some current developments in graph and text data analysis.
[LO5]  Prove competence in reporting the results and presenting the data with visualisation techniques.

Assessment strategy

The module will be assessed by a practical piece of coursework (50%) and a 2-hour unseen examination (50%).

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.

Bibliography

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

2. Stephen A. Sweet and Karen A. Grace-Martin (2010) Data Analysis with SPSS: A First Course in Applied Statistics. 4th Edition, Pearson. 
3. Roxy Peck, Chris Olsen, I Jay L. Devore (2011) Introduction to Statistics and Data Analysis 4th Edition. Brooks/Cole.
4. Mohammed J. Zaki, Wagner Meira Jr. (2014) Data Mining and Data Analysis. Cambridge University Press.
5. Stephen Few, (2009) Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press.
6. Richard Brath, David Jonker (2015) Graph Analysis and Visualization. Wiley.