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MIS41150

Academic Year 2025/2026

Intro to Business Analytics (MIS41150)

Subject:
Management Information Systems
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
7.5
Module Coordinator:
Dr Mark Connor
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

An overview of the field and the programme. This module will feature analytics case studies, and example simulation/role-play to help frame and contextualise discussion of the goals, methods, benefits and limitations of many business analytics techniques.

Topics:
● What is Business Analytics?
● The Analytics Organisation
● What are Businesses trying to achieve? (Measurement, Understanding, Prediction, Optimisation, Decision-making)
● Descriptive, Predictive, Prescriptive; Relationships to other topics in the programme including Business Intelligence, Visual Analytics, Optimisation, Machine Learning and Artificial Intelligence
● Ethical Considerations for Data and Analytics

About this Module

Learning Outcomes:

At the end of this module, students will be able to:

● Define Business Analytics and explain in detail its roles in the organisation
● Define a number of sub-topics of Business Analytics and explain how they relate to the organisation’s goals
● Map from verbal descriptions of business problems to appropriate analytics approaches
● Discuss ethical issues as they relate to Business Analytics and data
● Deliver and explain analytical outputs and their implications in real world business environments

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Specified Learning Activities

80
Autonomous Student Learning

80
Total

184

Approaches to Teaching and Learning:
Active/ Task Based Learning (Simulations, Case Studies, etc.)
Peer and Group Work (Assessment Projects)
Lectures

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Individual Project: A four part project consisting of the application of:
1. Descriptive Analytics (10%)
2. Predictive Analytics (10%)
3. Prescriptive Analytics (10%)
4. A simulated stakeholder meeting (30%)		 Week 7, Week 9, Week 11, Week 12 Graded Yes
60
 Yes
Group Work Assignment: Group Work Week 5 Graded No
40
 No

Carry forward of passed components
Yes
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
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
• Self-assessment activities

How will my Feedback be Delivered?

Not yet recorded.

Davenport, T.H. and Kim, J.H. (2013) Keeping up with the Quants: Your Guide to Understanding and Using Analytics.

Schmarzo, B. (2016). Big Data MBA: Driving business strategies with data science.

Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals.

Name Role
Dr Mark Connor Lecturer / Co-Lecturer
Dr Annunziata Esposito Amideo Lecturer / Co-Lecturer

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Tues 09:30 - 11:20
Autumn Lecture Offering 2 Week(s) - Autumn: All Weeks Tues 12:00 - 13:50
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