Introductory description

This module aims to enable participants to understand the principles, challenges and opportunities offered to organisations (particularly eBusinesses) by Big Data. The focus of the module will be primarily on the management implications, rather than technical specifics of a Big Data architecture and/or analytics (both of which are introduced). Following from this, the module will also focus on the visualisation of Big Data, and of the insights derived from Big Data analytics, to support management decision making.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

• Big Data Technologies
  o Core Concepts of Big Data
  o Data Warehouse Architecture
  o Big Data Architecture
  o Hadoop Workshop
• Analytics
  o Core Concepts of Analytics
  o Digital Analytics
  o Decision Analytics
  o Discovery Analytics
  o Advanced Analytics
• Decision Science & Visualisation
  o Key Topics in Decision Science
  o Decision Support Systems
  o Visual Communication
  o Data Visualisation
  o Data Visualisation Software
• Big Data and Visualisation in eBusiness Decision Making
  o Practical Simulation of the Above Topics

Learning outcomes

By the end of the module, students should be able to:

• Understand the key differences between Big Data technologies and analysis methods and traditional approaches.
• Identify examples and case studies where Big Data has been implemented to create organisational and/or societal benefits.
• Understand the basics of Big Data architecture and how to operate basic procedures within Hadoop.
• Understand the different types of analytics applications and critically appraise their use in different circumstances.
• Be able to perform different analytical methods and interpret the results.
• Understand the core topics and applications of decision sciences.
• Understand the core concepts of visual communication and data visualisation.
• Perform a variety of visualisation techniques.
• Evaluate the strengths and limitations of Big Data, analytics and visualisation techniques in organisational decision making.

Indicative reading list

Chen C, Hardle W and Unwin U (eds.). Handbook of data visualization. Berlin Heidelberg: Springer.
Davenport TH and Harris JG (2007). Competing on analytics: The new science of winning. Cambridge (MA): Harvard Business Press.
Few S (2006). Information dashboard design: The effective visual communication of data. Sebastopol, CA: O’Reilly Media, Inc.
Laursen G and Thorlund J (2010). Business analytics for managers: Taking business intelligence beyond reporting. Hoboken (NJ): John Wiley & Sons.
Mayer-Schönberger V and Cukier K (2013). Big data: A revolution that will transform how we live, work and think. London: John Murray.
Tufte ER (2001). The visual display of quantitative information (2nd Edition). Cheshire, (CT): Graphics Press.
Tufte ER (2006). Beautiful evidence. Cheshire, (CT): Graphics Press.

View reading list on Talis Aspire

Subject specific skills

Big data, analytics, visualisation, NoSQL, machine learning, decision science

Transferable skills

Critical analysis, data management, teamwork, software skills, programming