MIS3010S Analytics Modelling

Academic Year 2023/2024

With the availability of data and computing power, analytical and mathematical approaches have become increasingly important in addressing business, engineering and other problems; notably decision problems where a decision must be made subject to uncertainty or constraints such as limitations on resources. The objective of this course is to further develop your understanding of the application of quantitative analytical techniques for problem solving in business and management.

In this module we build on previous topics covered in the “Data Analysis for Decision Makers” and “Business Analytics” modules, developing the concept of Mathematical and Analytics Modelling in Decision Problems, and extending to probabilistic decision analysis and network modelling and solution techniques. You will learn how to conceptualise complex business problems in new ways and transform them into a model (mathematical programming formulation, decision tree or network) that describes the problem. For example, a decision tree model – when we are given a sequence of decisions and chance events with consequences and probabilities – may be used to understand the results of different alternatives and scenarios, and make decisions to optimise business profit or some other measure; or a network model – when we are given several distinct points (such as cities) and links connecting them (such as a road network) – may be used to determine the optimal routing of goods in a supply chain.

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Curricular information is subject to change

Learning Outcomes:

On completion of this module, you should be able to:

• Describe the main principles of mathematical modelling as they apply to decision problems and optimisation;
• Explain the details of a suite of key decision tree, mathematical programming and network approaches to problem solving;
• Apply these principles to improve the quality of analysis and decision-making;
• Discuss a portfolio of important business and other applications of these principles;
• Use mathematical modelling computer packages and information technology as an aid in decision making.

Indicative Module Content:

Indicative Module Content:

• Decision Modelling and Analysis. Structuring decisions: application of decision trees.
• Linear Programming (LP) advanced model formulation and applications;
• Implementing (LP) models in software, solving and interpreting solutions;
• Integer Programming (IP) models, solution and applications;
• Introduction to Graph Theory and Network problems;
• Network Optimisation: The minimum spanning tree problem and the shortest path problem.
• Social Networks and other Real World Networks: centrality measures; empirically observed network structures.

Student Effort Hours: 
Student Effort Type Hours
Lectures

20
Specified Learning Activities

85
Autonomous Student Learning

102
Total

207
Approaches to Teaching and Learning:
Students attend will classes for this module and have the opportunity to engage in active learning during these sessions. There will be in-class discussion and group work to analyse module concepts. Where appropriate, the module will incorporate case based learning 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Continuous assessment Varies over the Trimester n/a Graded No

40
Examination: End of Trimester examination 2 hour End of Trimester Exam No Graded No

60

Carry forward of passed components
No
 
Remediation Type Remediation Timing
Repeat Within Two Trimesters
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

General feedback is provided to students on all their submitted assessment components.

Name Role
Dr Christina Burke Tutor
Ms Michele Connolly Doran Tutor
Assoc Professor Sean McGarraghy Tutor
Rachel Sim Tutor
Caleb Tan Tutor
Chee Shong Tan Tutor
Charlene Tan Puay Koon Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 

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