MIS3003S Business Analytics

Academic Year 2023/2024

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 study, we learn how it works on
paper before proceeding to software-based implementations. We also
consider practical applications of these techniques, including some or
all of 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.

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Curricular information is subject to change

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.

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities


Autonomous Student Learning






Approaches to Teaching and Learning:
Students will attend 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
Assignment: Main Assignment Varies over the Trimester n/a Graded No


Examination: Online Examination 2 hour End of Trimester Exam No Graded No


Carry forward of passed components
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
Soh Cheong Hian Lecturer / Co-Lecturer
Dr Christina Burke Tutor
Ms Michele Connolly Tutor
Shirley Ho Tutor
Assoc Professor Sean McGarraghy Tutor
Dr Miguel Nicolau Tutor
Rachel Sim Tutor
Chee Shong Tan Tutor
Charlene Tan Puay Koon Tutor
Siti Zarifah 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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