MIS30040 Analytics Modelling

Academic Year 2020/2021

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 constraints such as limitations on resources; or data analytics problems where we seek useful information in a large dataset.

This course introduces the concept of Mathematical and Analytics Modelling in Decision Problems, and surveys some of the major mathematical models and solution techniques. Participants will learn how to conceptualise complex business problems and transform them into a set of equations (models) that describe the problem. For example 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.

We emphasise how to correctly formulate a mathematical model (given a real-world problem description), and the trade-offs necessary between comprehensiveness and usability.

Business problems can be modelled and solved using optimisation techniques such and Linear or (Mixed) Integer Programming. Participants will be introduced to the use of computer packages to implement models for sample problems and assignments. This module will have Face-To-Face delivery with lecture recordings made available online for those who cannot attend. The principles of Active Learning guide the face-to-face contact sessions, with students engaging in hands-on mathematical modelling exercises. Self-assessment exercises and additional online resources on Brightspace will complement the face-to-face sessions.

The assessments will be a mix of individual (MCQ) and group (problem modelling and solution interpretation) to provide opportunities for both individual deep engagement, and to improve teamwork and report writing skills.

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Curricular information is subject to change

Learning Outcomes:

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

• discuss a portfolio of important business optimisation problems and their application;
• explain the concepts of a suite of key linear programming and network approaches;
• formulate appropriate mathematical models of real world business optimisation problems;
• implement and solve the mathematical models using suitable computer packages such as FICO XpressMP and Microsoft Excel;
• demonstrate how to aid business decision making by interpreting the solutions to recommend decisions.

Indicative Module Content:

Linear Programme (LP) models and applications;
Implementing (LP) models and interpreting solutions;
Integer Programming (IP) models and applications;
Introduction to Graph Theory and Network problems;
Network Optimisation.

Student Effort Hours: 
Student Effort Type Hours
Lectures

24

Tutorial

0

Specified Learning Activities

42

Autonomous Student Learning

48

Total

114

Approaches to Teaching and Learning:
A blended approach will be used with face-to-face lectures recorded and made available on Brightspace for those who cannot attend in person. Activities include:
active/task-based learning;
(virtual) group work;
face-to-face lectures (which will be recorded and posted on Brightspace);
self-assessment practice exercises on Brightspace;
Online drop-in clinics. 
Requirements, Exclusions and Recommendations
Learning Requirements:

You need some mathematical background for this course. For the first part, Linear Programming, you need to understand how to solve systems of linear equations (e.g., using Gaussian Elimination). You should be familiar with the concept of a linear inequality. This is dealt with in most UCD 1st year Mathematics courses covering Linear Algebra, e.g., Commerce, Engineering, Science, etc. The second part of the course, Networks, requires some understanding of sets and indexed notation. Everything else will be covered in this course.

Learning Recommendations:

Some experience with computers is helpful but not required: you will be shown how to use any computer applications required. Understanding algorithms (step-by-step approaches to problem solving) is also helpful.


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

30

Assignment: End of semester individual assignment Coursework (End of Trimester) n/a Graded No

70


Carry forward of passed components
No
 
Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback
• Self-assessment activities

How will my Feedback be Delivered?

Solutions to self-assessment exercises on VLE; Automated feedback on MCQ; Team feedback (Grade plus comment) pre team project, plus general feedback to the class.