MODULE SPECIFICATION TEMPLATE
MODULE DETAILS
Module title
Module code
Credit value
Level
Big Data and Business Intelligence
OS301
Mark the box to the right of the
appropriate level with an ‘X’
Level 4
Level 5
Level 6
Level 0 (for modules at foundation level)
X
Level 7
Level 8
Entry criteria for registration on this module
Pre-requisites
Specify in terms of module codes or
equivalent
Co-requisite modules
Specify in terms of module codes or
equivalent
Module delivery
Mode of delivery
Taught
Other
X
Distance
Placement
Pattern of delivery
Weekly
X
Block
Other
Online
When module is delivered
Semester 1
Semester 2
Throughout year
X
Other
Brief description of module Exploring current issues and emerging uses of big data and business
content and/ or aims
intelligence. This research elective enables students to conduct in-depth
Overview (max 80 words)
research into a topic of their choice. Topics are wide ranging and include:
data analytics, data mining, data warehousing, the business application of
neural networks, sentiment analysis and data visualisation.
The module invites the student to consider how the growth of big data
impacts on business opportunities and risks. In this dynamic field
identification of new research topics is encouraged.
Module team/ author/
Asher Rospigliosi
coordinator(s)
Stelios Kapetanakis
Ray Bachan
Clare Millington
Andrea Benn
Jeff Readman
School
Business School
Site/ campus where
Moulsecomb
delivered
Course(s) for which module is appropriate and status on that course
Course
BSc Management all pathways
BSc Business all pathways
Module descriptor template: updated Aug 2013
Status (mandatory/ compulsory/
optional)
Optional
Optional
MODULE AIMS, ASSESSMENT AND SUPPORT
Aims
This module aims to provide a tutor and peer-supported learning
environment where students can research a topic of particular interest
within the broad context of big data, business intelligence and data
visualisation.
Learning outcomes
Subject-specific: students will be able to:
 research contemporary uses and developments in big data and
business intelligence
 retrieve a large data set and apply suitable tools of quantative
analysis
Cognitive: students will be able to:
 Identify a topic for research and frame an appropriate research
question.
 Evaluate sources of data and issues regarding retrieval and
analysis
 Conduct desk research i.e. secondary research.
 Review a wide range of published data (e.g. academic
literature, company, trade and government publications etc).
 Identify, retrieve and manage a large set of publicly available
data
 Analyse and synthesise relevant data with an appreciation of
statistical validity.
 Summarise ‘best practice’ and indicate a possible solution to
the problem and / or a worthwhile approach to the collection
and analysis of a data set.
 Present their studies in a clear, concise and appropriately
structured report.
Content
Big data refers to data sets so complex and large they are
unwieldy to process using conventional databases. They result
from the increasingly ubiquitous use of real time computing in
processes such as web analytics, government, logistics,
telecommunications, social media, security, open data and
healthcare.
Business intelligence draws on, visualises and analyses a
firm's internal data from sources such as transactional
databases.
Students will have a free choice of topic subject to approval by
the staff team.
Research will normally be conducted into one of:

Marketing information

e-business

Big data in health, security, science or business

Business intelligence in performance improvement,
Module descriptor template: updated Aug 2013
prediction and forecasting

Dashboards & data visualization

Social or organisational implications of big data
Potential topic areas include:

Mining open data sources for entrepreneurial
opportunities

Does big data and privacy: a panopticon on all our
actions?
Learning support

How big data influences how we live

How business intelligence impacts on how we work

Evaluating the business case for big data

Information strategy and governance
Indicative books:
Arthur L (2013) Big Data Marketing: Engage customers more
effectively and drive value, Wiley.
Brynjolfsson E & McAfee A (2014) The Second Machine Age, Norton.
Davenport TH (2014) Big Data at Work, Harvard Business Review
Press.
Davenport TH (2013) Predictive Analytics, Wiley.
Foreman JW (2013) Data Smart: Using data science to transform
information into insight, Wiley.
Howson C (2013) Successful Business Intelligence: Unlock the value of
BI and big data, McGraw-Hill.
Marz N & Warren J (2014) Big Data: Principles and best practices of
scalable realtime data systems, Manning.
Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that
will transform how we live, work, and think, Houghton Mifflin Harcourt.
Minelli M, Chambers M & Dhiraj A (2013) Big Data, Big Analytics:
Emerging business intelligence and analytic trends for today's
businesses, Wiley.
Ohlhorst F (2013) Big Data Analytics, Wiley.
Paharia R (2013) Loyalty 3.0: How to revolutionize customer and
employee engagement with big data and gamification, McGraw-Hill.
Provost F & Fawcett T (2013) Data Science for Business, O'Reilly
Media
Module descriptor template: updated Aug 2013
Sanders NR (2014) Big Data Driven Supply Chain Management: A
framework for implementing analytics and turning information into
intelligence, Pearson Financial Times.
Schmarzo B (2013) Big Data: Understanding how data powers big
business, Wiley.
Shroff G (2013) The Intelligent Web: Search, smart algortithms and big
data, Oxford University Press.
Simon P (2014) The Visual Organization: Data visualization, big data,
and the quest for better decisions, Wiley.
Indicative journals:
ACM Transactions on Information Systems
ACM Transaction on Internet Technology
Communications of The ACM
Computer Fraud and Security
Electronic Commerce Research And Applications
Electronic Journal For Information Systems Evaluation
European Journal of Information Systems
European Journal of Marketing
Industrial Management & Data Systems
Information Management & Computer Security
Information Systems Journal
Information Systems and E-Business Management
Information Systems Management
Information Systems Research
Information Systems Security
Information Technology and People
International Journal of Cooperative Information Systems
International Journal of Electronic Commerce
International Journal of Information Management
International Journal of Information Technology
International Journal of Information Technology & Decision Making
International Journal of Research In Marketing
International Marketing Review
Journal of Information Technology
Journal of Management Information Systems
Journal of Organizational Computing And Electronic Commerce
Journal of Strategic Information Systems
Marketing Intelligence and Planning
Quarterly Journal of Electronic Commerce
Business Intelligence Journal
International Journal of Business Intelligence and Data Mining
International Journal of Business Intelligence Research
Teaching and learning activities
Details of teaching and
learning activities
Course material will be introduced via workshops, guided self-study
and online resources. Workshops will focus on developing a practical
methodology.
Allocation of study hours (indicative)
Where 10 credits = 100 learning hours
Module descriptor template: updated Aug 2013
Study hours
SCHEDULED
This is an indication of the number of hours students can expect to
spend in scheduled teaching activities including lectures, seminars,
tutorials, project supervision, demonstrations, practical classes and
workshops, supervised time in workshops/ studios, fieldwork, and
external visits.
20
GUIDED INDEPENDENT
STUDY
All students are expected to undertake guided independent study
which includes wider reading/ practice, follow-up work, the
completion of assessment tasks, and revisions.
180
PLACEMENT
The placement is a specific type of learning away from the University.
It includes work-based learning and study that occurs overseas.
TOTAL STUDY HOURS
200
Assessment tasks
Details of assessment on
this module
An individual demonstration of a technical task requiring engagement
with retrieving and analysing data sets (20%)
An individual initial project proposal (10%)
An individual project, up to 4,000 words (70%)
Types of assessment task1
% weighting
Indicative list of summative assessment tasks which lead to the award of credit or which are required for
progression.
(or indicate if
component is
pass/fail)
WRITTEN
Written exam
COURSEWORK
Written assignment/ essay, report, dissertation, portfolio, project
output, set exercise
PRACTICAL
Oral assessment and presentation, practical skills assessment, set
exercise
100%
EXAMINATION INFORMATION
Area examination board
Refer to Faculty Office for guidance in completing the following sections
External examiners
Name
Position and institution
Date appointed
Date tenure
ends
Please see Studentcentral
QUALITY ASSURANCE
Date of first approval
Only complete where this is not the
first version
Date of last revision
Only complete where this is not the
first version
1 Set exercises, which assess the application of knowledge or analytical, problem-solving or evaluative skills, are included
under the type of assessment most appropriate to the particular task.
Module descriptor template: updated Aug 2013
Date of approval for this
version
June 2014
Version number
1
Modules replaced
Specify codes of modules for which
this is a replacement
Available as free-standing module?
Module descriptor template: updated Aug 2013
Yes
X
No