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MIS3008S

Academic Year 2024/2025

Data Analysis for Decis Makers (MIS3008S)

Subject:
Management Information Systems
College:
Business
School:
Business
Level:
3 (Degree)
Credits:
10
Module Coordinator:
Dr Miguel Nicolau
Trimester:
Autumn and Summer (separate)
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

In the era of "big data", there is a challenge to turn data into insight. Data analysis is the application of statistical techniques to describe and explore a set of data with the objective of highlighting useful information. This module is a foundation in data analysis for business students and aims to serve the needs of subsequent courses in areas such as marketing, finance, accounting and business analytics.

Quantitative analysis and descriptive statistics: how to gather and interpret large volumes of data in order to describe the information in concise and useful ways. For example, what is the average spend of a sample of customers in a coffee shop.

Probability and inferential statistics: how to infer population parameters from sample statistics. For example, how much is likely to be spent in the coffee shop in total.

Introduction to predictive modelling: how to estimate future values based on an extension of current trends. For example, estimate sales next year based on sales in the last 10 years.

About this Module

Learning Outcomes:

On completion of this module, students should be able to:
Prepare spreadsheet models to store, manipulate and analyse quantitative data using common probability distributions and statistical functions.
Calculate, analyse and present useful statistical measurements from large-scale data sets.
Create and interpret inferential statistical statements about population parameters.
Interpret the results of data analyses with a view to informing decision making,
Create and interpret simple linear regression models.

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

85
Autonomous Student Learning

125
Lectures

20
Total

230

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 Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Continuous Assessment Week 7 Standard conversion grade scale 40% No
40
 No
Exam (In-person): Examination Week 15 Standard conversion grade scale 40% No
60
 No

Carry forward of passed components
No
 

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
Jonas Lee Lecturer / Co-Lecturer
Assoc Professor Sean McGarraghy Lecturer / Co-Lecturer
Dr Christina Burke Tutor
Soh Cheong Hian Tutor
Hafihz Noor Tutor
Chandra Reddy Tutor
Rachel Sim Tutor
Caleb Tan Tutor
Chee Shong Tan Tutor
Charlene Tan Puay Koon Tutor
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