Subject Outline

Submitted by sylvia.wong@up… on Tue, 12/29/2020 - 11:52

Sub Topics

Subject Code	BUS202
Subject Name	Business Intelligence for Decision
Level	200
Credit Points	3.00
Type	Core
Pre/Co-requisite	None

Successfully completing this subject will require you to commit to a balance of class time, pre and post work and online activities as outlined below.

Total workload

Total No. timetabled hours:	Total No. personal study hours:	Total workload hours:
33	77	110

Weekly study workload

Timetabled:	Personal study:	Total:
3 hours per week	7 hours per week	10 hours per week

Description and rationale

This subject will enable the student to understand business analytics modelling through exploring statistical techniques which are necessary for the modern business environment. The subject covers areas such as data exploration and visualization in business, information technology, various analytical methods used in decision making. Upon completion of the subject, students will achieve the required insight into how business analytics can be used as a business decision supporting tool to achieve competitive advantages in various business contexts. 

Topics to be covered

Topics are often refined and subject to change. Please refer to the topics and subtopics listed in the LMS menu for this module.

Description

Learning Outcome Description		Assessment tasks in which this learning outcome is assessed
a	Demonstrate the ability to identify a range of different types of business information in order to achieve competitive advantage in a range of business contexts.
b	Demonstrate the ability to identify appropriate analytical tools and interpret data variables with the changes over time to make informed business decisions within a range of business settings.
c	Examine and effectively communicate a range of business strategies and predictions, and evaluate different decision-making tools for a selected population in a range of business environments.
d	Discover the significance of business intelligence by distinguishing between descriptive, predictive, and prescriptive business decision making and its application across a range of business contexts.

Assessment tasks

Assessment Task		Weighting	Assessment Due	Subject Learning Outcomes to be assessed
1	Data presentation (Individual)	30%	Week 3	a & b
2	Case Study Analysis: business case scenario to identify data and present a trend analysis. (Individual)	30%	Week 10	c & d
3	Case Study Analysis: business case scenario to identify appropriate data and propose data driven solutions. (Individual)	40%	Week 12-13	a, b, c & d

Submitting your assessment tasks

Most assessment tasks are submitted using the Learning Management System. For more instructions on submitting the assessment tasks and specific information of the subject assessment submission requirements, please refer to the instructions in the Learning Management System.

Late submission and extension

There are penalties for late submission of assessment tasks. Please refer to the assessment section in the Learning Management System for more information on late submission penalties.

If you would like to request an extension for a submission deadline of your assessment, you need to meet the eligibility requirements.

Please refer to the assessment section in the Learning Management System for more information on late submission penalties, and requests for extensions.

Recommended learning and reading list

Textbooks

Anderson, DR, Sweeney, DJ, Williams, TA, Camm, JD, and Cochran, JJ, 2017, Essentials of statistics for business and economics 8th edn., New York, US: Cengage Learning.  

Graham, LR, McNaughton, ML, & Mansingh, G 2020, Business intelligence for small and medium-sized enterprises: An agile roadmap towards business sustainability, Taylor & Francis Group, Boca Raton, FL. 

Grossmann, W & Rinderle-Ma, S 2015, Fundamentals of Business Intelligence, 1st edn., Springer-Verlag Berlin Heidelberg. 

Jackson, TW & Lockwood, S 2018, Business analytics: A contemporary approach, Red Globe Press. 

Marr, B 2016, Big data in practice: how 45 successful companies used big data analytics to deliver extraordinary results, John Wiley & Sons. 

Thomson, J.K & Laney, D. B (2020), Building analytics teams: harnessing analytics and artificial intelligence for business improvement, Packt Publishing, Birmingham UK 

Trivedi, SH, Dey, S, Kumar, A, & Panda TK 2017, Handbook of research on advanced data mining techniques and applications for business intelligence, IGI Global, PA, USA. 

Vidgen, R, Kirshner, S, & Tan, F 2019, Business analytics. A management approach, 1st edn., Palgrave, UK. 