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MIS20010

Academic Year 2025/2026

Business Analytics (MIS20010)

Subject:
Management Information Systems
College:
Business
School:
Business
Level:
2 (Intermediate)
Credits:
5
Module Coordinator:
Dr Miguel Nicolau
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Business Analytics focuses on the use of mathematical, statistical, data-oriented and computer-based techniques to help businesses operate optimally. It seeks to understand what has happened and predicting what is going to happen, and making optimal decisions to take advantage of that.

The main techniques we consider are Correlation, Regression, Time-Series Forecasting, Linear Programming, Classification, and Clustering. For each technique, we learn how it works on paper before proceeding to software-based implementations. We also consider practical applications of these techniques, including sales forecasting, credit risk, resource allocation, employee assignment, investment, customer churn, inventory, and product mix.

Emphasis is placed not only on formulating and using models to obtain solutions, but on understanding case study problems, choosing the appropriate models, and interpreting and critiquing the results.

This module is unique in the sense that it not only offers a strong understanding of how to use data to drive decision making in the business world, but also that many of the quantitative techniques that are taught are foundations for advanced Machine Learning and Artificial Intelligence, which are at the core of Business decision-making nowadays.

About this Module

Learning Outcomes:

On completion of this module, students should be able to understand a range of quantitative business problems and identify suitable analytics models for addressing them; and be able to explain, carry out in practice, and interpret the results of models including regression, time series forecasting, correlation, linear programming, classification, and clustering.

Indicative Module Content:

* Linear correlation and regression techniques for data understanding and prediction;
* Linear Programming and sensitivity analysis for business optimisation, including transportation problems and integer linear programming;
* Linear and non-linear classification techniques, including linear class boundary optimisation, loss functions, and KNN;
* Clustering and its role as an unsupervised learning tool for decision making.

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:
* Face-to-face lectures;
* Practical sessions;
* Online self-learning documents;
* Real-world group project, including software component and reporting.

Requirements, Exclusions and Recommendations
Learning Exclusions:

Introduction to Business Analytics (1st-year module from the BSc in Quantitative Business)

Learning Recommendations:

This module requires that students already have knowledge equivalent to UCD MIS10090, Data Analysis for Decision Makers -- probability and statistics, and basic Excel. The level of required mathematics is about Ordinary Level Leaving Certificate, occasionally a bit higher.


Module Requisites and Incompatibles
Incompatibles:
MIS10060 - Introduction to Bus Analytics


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): Computer-based examination, taken in UCD End of trimester
Duration:
2 hr(s)
 Standard conversion grade scale 40% No
50
 No
Group Work Assignment: Group project involving the use of real data sourced by the students, going through all analytics phases: descriptive, predictive, and prescriptive. Week 10 Standard conversion grade scale 40% No
50
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Autumn 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?

Not yet recorded.

Name Role
Dr Mel Devine Lecturer / Co-Lecturer
Ms Bing Chen Tutor
Jiajun Duan Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 23, 24, 25, 26, 29, 30, 32, 33 Mon 15:00 - 16:50
Spring Lecture Offering 2 Week(s) - 20, 21, 23, 24, 25, 26, 29, 30, 32, 33 Mon 11:00 - 12:50
Spring Small Group Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 15:00 - 15:50
Spring Small Group Offering 2 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 14:00 - 14:50
Spring Small Group Offering 3 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 11:00 - 11:50
Spring Small Group Offering 4 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 12:00 - 12:50
Spring Small Group Offering 5 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 16:00 - 16:50
Spring Small Group Offering 6 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 10:00 - 10:50
Spring Small Group Offering 7 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 09:00 - 09:50
Spring Small Group Offering 8 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 10:00 - 10:50
Spring Small Group Offering 9 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 11:00 - 11:50
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