Analytics Implementation II: Visualising Data for Effective Communication

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• read structured and unstructured data stored in popular formats such as excel worksheets, text tables into a visualisation application such as SAS JMP or SPSS Visualisation Designer;
• choose the most appropriate visualisation technique by considering the purpose of the forecasting method using various types of information criterion.

Transferable and Generic Skills

Having successfully completed this module you will be able to:

• use your analytic skills in problem solving;
• communicate effectively, in both graphical and written form, using and justifying argument within reports and presentations.
• plan and control effectively for successful completion of a personal workload;
• self-manage the development of learning and study skills;

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• the importance of accuracy, clarity and creativity in effective data visualisation.
• the most widely used visualisation techniques, their capabilities as well as limitations in analyzing large data sets;
• the nature and unique challenges of Big Data;

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• use a visualisation package to produce a visualisation of a large dataset.

1.What is big data? Its nature, its unique challenges and common areas of application

2.Why pictures speak a thousand words: Theoretical perspectives on visualisation as a communication tool: Gestalt Principles of Perception; Psychological Perspectives; Colour and Notation;

3.Software: Introduction to analytics software for visualisation, such as SAS JMP, SPSS Visualisation Designer and Tableau Desktop

4.Dressing up and displaying naked numbers for best effect

5.Visualisation by counts and rates of occurrences

6.Visualisation around means and across differences

7.Visualising relationships

8.Visualising networks, groups and cluster patterns

9.Pictures for predictive modelling

10.Visualising textual data, e.g. 'The discography of Prince'

11.Big data on small screens: Visualising for mobile audiences.

The module will adopt a lab based, collaborative, active learning approach.

Contact hours will comprise 12, 3-hour lectorials, i.e. sessions integrating lectures and lab tutorials, which will attempt to combine the intimate learning and teaching environment of the tutorial within a large class. The typical lectorial will start with a one-hour lecture on a visualisation technique. Lab will work will then reinforce learning on the technique introduced by the lecture. Working in small groups of up to four, students will then be set a problem involving applying the visualisation technique under discussion by analysing a large dataset allocated to them.

It is anticipated that there will be at least one lecture delivered by an external speaker from the data analytics industry.

Lab work will be based on data generated from a variety of research projects funded by ESRC: All students on the module will be registered for the UK Data Service and data for learning and teaching will be accessed via this service.

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Class participation

• Assessment Type: Formative
• Feedback: •Verbal formative feedback will be given by the lecturer/tutor throughout the module during lectorials and during one-to-one sessions during office hours. •Written feedback will also be provided by the lecturer/tutor through email communications. •Peer-to-peer feedback will also be key for developing and supporting learning objectives
• Final Assessment: No
• Group Work: No

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.