MIS10060 Introduction to Business Analytics

Academic Year 2022/2023

Business Analytics is about using mathematical modelling, statistics, and computer techniques to achieve understanding and help principled decision-making in the context of business. In terms of understanding, this involves trying to gain insights into unknown underlying trends and patterns in data, which can be extremely valuable and offer new business opportunities. The insights provided by the quantitative analysis of available data can substantially reduce the amount of uncertainty in the potential outcomes of business decisions, greatly enhancing the decision making process.

The module material will be delivered via a mix of lectures, tutorials and online lectures (blended learning).

This module provides an introduction to how businesses are embracing the “Analytics” era, and introduces some of the simple tools used for predictive modelling and optimisation for decision making. These include regression and classification for discovering patterns in numerical data, and linear programming for the optimal allocation of available resources in an efficient manner.

The module is both about achieving understanding of the concepts, and about learning to apply them in practice. Learning is via lectures (a mix of both in class and online), tutorials, readings, videos, and practice exercises. Students will carry out work on paper and on computer, including calculations, spreadsheet programming, and written and oral reporting. The assessments will be a mix of individual and group to provide opportunities for both individual deep engagement, and to improve teamwork skills.

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Curricular information is subject to change

Learning Outcomes:

On completion of this module, students should be able to:
1) identify the main classes of analytics problems arising in business and industry;
2) formulate, explain, and distinguish between various forms of regression, classification, and linear programming models;
3) create and implement regression, classification and linear programming models (in suitable software);
4) test the models and interpret results in a form suitable for a business client or manager.

Indicative Module Content:

Introduction to Business Analytics;
Data Management;
Linear Regression;
Classification;
Linear Programming.

Student Effort Hours: 
Student Effort Type Hours
Lectures

24
Small Group

12
Specified Learning Activities

36
Autonomous Student Learning

40
Total

112
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 Exclusions:

MIS20010


Module Requisites and Incompatibles
Incompatibles:
COMP10030 - Algorithmic Problem Solving, MIS20010 - Business Analytics


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: End of semester individual assignment Coursework (End of Trimester) n/a Graded No

70
Continuous Assessment: Assignments/Mini-Projects/MCQ Throughout the Trimester n/a Graded No

30

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?

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