Course Code: ANL551
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Synopsis

Data Analytics for Decision Making presents Data Analytics as a key modern approach for decision making in business organisations. It examines the key aspects of Business Analytics based on Cross-Industry Process for Data Mining (CRISP-DM) framework. Students will learn to apply CRISP-DM by going through a series of projects involving data visualisation, association rule mining, clustering, predictive modelling and response modelling. By walking students through such projects, they will gain experience in turning data into important insights that may improve organisational performance.
Level: 5
Credit Units: 5
Presentation Pattern: EVERY JAN

Topics

  • Introduction to Business Analytics
  • CRISP-DM--- An Overview
  • Introduction to Data Visualisation
  • Process and challenges in a Data Visualisation project
  • Association Rule Mining
  • Process and challenges in an Association Rule Mining project
  • Data Clustering
  • Process and challenges in a Data Clustering project
  • Predictive Modelling
  • Process and challenges in a Predictive Modelling project
  • Response Modelling
  • Process and challenges in a Response Modelling project

Learning Outcome

  • Design Analytics Solutions using the CRISP-DM framework
  • Appraise the suitability of analytics techniques in different contexts
  • Evaluate performance of analytics models
  • Assess the quality of data for analytics
  • Prepare data for mining and analysis
  • Construct an analytics solution using application software
Part-time Programmes / CET Courses

Date and Duration

DayDateWeekTime
Wednesday14 Jan 2026107:00 pm - 10:00 pm
Wednesday21 Jan 2026207:00 pm - 10:00 pm
Wednesday28 Jan 2026307:00 pm - 10:00 pm
Wednesday4 Feb 2026407:00 pm - 10:00 pm
Wednesday11 Feb 2026507:00 pm - 10:00 pm
Saturday14 Feb 2026512:00 pm - 03:00 pm


Target Audience

Executive roles requiring proficiency in data management and data analysis.

 

Relevance of Course to employment/upskilling/reskilling

ANL551 Data Analytics for Decision Makers supports employment, upskilling, and reskilling by building learners’ ability to interpret analytical outputs and translate them into sound, actionable business decisions. The course strengthens data-driven thinking, helps professionals evaluate trade-offs and risks, and improves confidence in using evidence to justify recommendations—capabilities valued across management, operations, marketing, finance, and strategy roles in data-driven organisations.


Admissions pre-requisite

Refer to graduate CET admissions and also include Admission Eligibility Criteria for Graduate CET Modular Courses (Admission Eligibility Criteria for Graduate CET Modular Courses).

 

Schedule

TimeAgendaDeliverables
Session 1

Introduction to Business Analytics

  • 19:00 – 19:30 Introduction to Business Analytics 
  • 19:30 – 20:00 Installing the IBM SPSS Modeler Load and read data file using Modeler
  • 20:00 – 20:30 CRISP-DM--- An Overview
  • 20:30 – 20:45 Break
  • 20:45 – 21:30 (Basic) Introduction to Data Visualisation
  • 21:30 – 22:00 Additional hands-on activities and In-class group discussions
-
Session 2

Introduction to Data Visualisation

  • 19:00 – 19:30 Introduction to Data Visualisation
  • 19:30 – 20:30 Process and challenges in a Data Visualisation project
  • 20:30 – 20:45 Break
  • 20:45 – 21:30 Creating and using interactive visualisation
  • 21:30 – 22:00 Additional hands-on activities and In-class group discussions
-
Session 3

Analytics Project: Association Analysis

  • 19:00 – 19:30 Association Rule Mining
  • 19:30 – 20:30 Process and challenges in an Association Rule Mining project 
  • 20:30 – 20:45 Break
  • 20:45 – 21:30 Apriori Algorithm
  • 21:30 – 22:00 Additional hands-on activities and In-class group discussions
-
Session 4

Analytics Project: Data Clustering

  • 19:00 – 19:30 Data Clustering
  • 19:30 – 20:30 Process and challenges in a Data Clustering project 
  • 20:30 – 20:45 Break
  • 20:45 – 21:30 K-Means Algorithm
  • 21:30 – 22:00 Additional hands-on activities and In-class group discussions
TMA Due:
Before Session 5
Session 5

Analytics Project: Predictive Modelling 

  • 19:00 – 19:30 Predictive Modelling
  • 19:30 – 20:30 Process and challenges in a Predictive Modelling project 
  • 20:30 – 20:45 Break
  • 20:45 – 21:30 Decision Tree Algorithm
  • 21:30 – 22:00 Additional hands-on activities and In-class group discussions
-
Session 6

Analytics Project: Response Modelling 

  • 12:00 – 12:30 Response Modelling
  • 12:30 – 13:30 Process and challenges in a Response Modelling project 
  • 13:30 – 13:45 Break
  • 13:45 – 14:15 Gain Chart
  • 14:15 – 15:00 In-class group discussions and/or In-class assessment
ECA Due:
2-weeks after Session 6

 

Assessments

Assignments, Online Test, Others, Written Exam, PARTICIPATION

 

Trainer info

A/P James Tan is an Associate Professor of the Business Analytics Programme in the School of Business at the Singapore University of Social Sciences. He has a PhD in Information Technology from Monash University, Australia. His primary research interests lie in machine learning and applied artificial intelligence for business. James actively partners with both local and international organizations to create novel analytics solutions for real-world problems. His notable collaborations include developing a new anomaly detection technology for data streams with the US Air Force Office of Scientific Research, and formulating strategies to initiate analytics projects for the social service sector. Locally, he has been leading many analytics projects that help SMEs improve their organizational performance.

Dr. Zhang Yimiao is a Senior Lecturer and Deputy Head of the Business Analytics Programme at the Singapore University of Social Sciences (SUSS). She received her PhD in Information Systems from Nanyang Business School, Nanyang Technological University. Her research examines individual behavior in online environments such as social media, e-commerce, and blockchain-based platforms, with the aim of informing more effective decision-making by both individuals and digital platforms. At SUSS, she teaches a range of undergraduate and postgraduate courses in text mining and data mining.

 

Course Completion requirements

Participants are required to achieve at least 75% attendance and pass any prescribed examinations/assessments or submit any course/project work (if any) under the course requirement.

  • Participants are required to complete all surveys and feedbacks related to the course
  • The course fees are reviewed annually and may be revised. The University reserves the right to adjust the course fees without prior notice.
  • Singapore University of Social Sciences reserves the right to amend and/or revise the above schedule without prior notice

 

Course Fees, payment and refund policy

 International Participants Singapore Citizens (below 40yrs), Permanent Residents Singapore Citizens (40yrs and above) SkillsFuture Mid - Career Enhanced Subsidy Enhanced Training Support for SME (Singaporean and PRs)
Course Fees (A) $3,168.00 $2,640.00 $2,640.00 $2,640.00
SSG Grant (70%) (B)  $1,848.00 $1,848.00 $1,848.00
Nett Course fees (A) - (B) = (C) $3,168.00 $792.00 $792.00 $792.00
9% GST on Nett course fees (D) $285.12 $71.28 $71.28 $71.28
Total nett course fees payable including GST (C) + (D) $3,453.12 $863.28 $863.28 $863.28
Less additional funding if eligible under various schemes (F) $-   $-   $528.00 $528.00
Total nett course fees payable including GST, after additional funding form the various schemes (E) - (f) = (H) $3,453.12 $863.28 $335.28 $335.28

For the various payment mode, please refer here.
For the refund policy, please refer here. 

 

For clarification, please contact the SUSS Academy via the following:
Telephone: +65 6248 0263
Email: [email protected]