INMR89-Big Data in Business

Module Provider: Business Informatics, Systems and Accounting
Number of credits: 20 [10 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2019/0

Email: k.nakata@henley.ac.uk

Type of module:

Summary module description:

This module focusses on the methods and techniques of using Big Data in business. Given the availability of large amounts of data in business and organisation, there is an increasing need for organisations to assess how effectively Big Data can be utilised for business. In this module, students consider how organisations can benefit from Big Data, and analyse business and technological requirements to create value though Big Data and business analytics. Students will also explore recent developments in technologies surrounding Big Data such as text analytics, cognitive analytics and visualisation, and assess types of tools that can be utilised, including the use of state-of-the-art analytics tools. 

Aims:

The aim of this module is for students to be able to evaluate the business value in utilising Big Data and develop information management solutions with an appreciation of Big Data and business analytics methods and technologies. 

Assessable learning outcomes:

Upon successful completion of this module, students should be able to: 

• Assess the business opportunity and value creation through the utilisation of Big Data and business analytics by analysing the business environment and requirements; 


• Critically assess suitable Big Data technologies and business analytics approaches; 


• Formulate a solution for achieving value through Big Data; 


• Demonstrate the solution using an existing Big Data and business analytics tool; 


• Assess the organisational and technical impact of implementing the solution. 

Additional outcomes:

Upon successful completion of this module, students should be able to: 

• Critically assess the suitability of a range of business analytics tools against a set of requirements; 


• Become familiar with state-of-the-art developments and commercial tools such as cognitive computing 

Outline content:

• Introduction; Business opportunity in the era of Big Data 


• Big Data Strategy 


• Business analysis for big data and business intelligence 


• Methods, techniques and tools for Big Data 


• Developing a Big Data strategy 


• Big Data visualisation 


• Artificial intelligence and machine learning 


• Professional, leadership and ethical issues in Big Data solutions 


• Emerging issues and impacts of Big Data 

Brief description of teaching and learning methods:

This module combines lecture, seminars and practical workshops to develop Big Data strategies. It also uses state-of-the-art analytics tools as part of developing a Big Data solution in business as a team project. 

Summative assessment- Examinations:
None

Summative assessment- Coursework and in-class tests:

Assessment will consist of a written coursework assignment (20 pages of A4) (100%) due on week 34. In completing the coursework assignment, students will be expected to work in teams to produce a big data strategy for a particular business context, based on which an individual report is produced. 

The assignment will provide students an opportunity to communicate critically and concisely their findings which demonstrate their extended understanding of the subject.

Formative assessment methods:

Feedback on the team project as well as topics related to assessable elements of the module will be provided during workshop sessions. 

Penalties for late submission:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx

Assessment requirements for a pass:

Students will be required to obtain a mark of 50% or above based on the coursework. 

Reassessment arrangements:

By re-submission of the coursework. 

Additional Costs (specified where applicable):

Last updated: 8 April 2019