Course Code: NPM513
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Synopsis

NPM513 Data Visualisation and Analytics for Social Sector explores the application of data visualisation and analytics to drive changes in the social sector. The course covers the construction of effective charts and dashboards, dashboard design principles, and data mining techniques such as association, clustering, and predictive modeling. Students will gain hands-on experience in applying analytics for a range of social good initiatives, including improving resource allocation and enhancing program effectiveness among others.
Level: 5
Credit Units: 5
Presentation Pattern: EVERY REGULAR SEMESTER

Topics

  • Data attributes
  • Charts appropriate for different types of data
  • Types of dashboards
  • Data preparation for data mining
  • Data mining methodology
  • Data mining techniques
  • Limitations & Ethical considerations of data mining
  • Predicting potential beneficiaries or donors
  • Volunteer segmentation and management
  • Targeted marketing & effective campaigns
  • Retaining engagement
  • Data-Driven approaches for social interventions

Learning Outcome

  • Evaluate the appropriateness of Data Visualisation techniques based on given data.
  • Critique the design of the dashboard based on the design principles.
  • Examine the different modelling techniques used.
  • Assess the different data mining applications used.
  • Construct a dashboard using Tableau.
  • Recommend the appropriate analytics techniques for different business problems.
  • Evaluate the application of business analytics for social good.
  • Formulate possible applications of business analytics for social good.
Part-time Programmes / CET Courses

Dates for 2026 January Semester

SessionTimeLocation
18:30am to 5:30pmSUSS
28:30am to 5:30pmSUSS
38:30am to 5:30pmSUSS
48:30am to 5:30pm
SUSS


Who Should Attend

  • Data Visualiser, Business Analyst, Strategic Planning, Data Scientist, Social Researcher

 

Relevance of Course to Employment / Upskilling / Reskilling

The social sector in Singapore is rapidly transforming as organisations adopt data-driven approaches to improve service delivery, optimise limited resources, and demonstrate measurable social impact. In this landscape, professionals who can translate complex data into actionable insights are in high demand across charities, social service agencies (SSAs), government-linked agencies, community organisations, and corporate CSR units.

It will help in enhancing employability in data-intensive roles across social and public sectors , supports career transition into emerging data roles, enables the improvement of programme effectiveness and resource allocation, strengthens organisational capability in data-driven decision making & aligns with Singapore’s digital transformational goals.


Admission Prerequisites

  • Bachelor's Degree or equivalent
Please refer to Graduate CET Admission Eligibility Criteria for Graduate CET Modular Courses.

 

 

Schedule

SessionTimeTopic
Session 1
Face to Face Session 		8:30am – 12:30pm
Data Visualisation: Data & Charts
  • Data representation and different forms of data
  • Best practices in selecting appropriate charts

12:30pm to 1:30pmLunch Break
 1:30pm to 5:30pm
Business Performance Measurement
  • Understanding business performance measurement
  • 7 basic principles that provide the scope of what constitutes a Business Performance Measurement system
  • Designing a Balanced Scorecard
Dashboards
  • Understanding the different types of dashboards
  • Best practices in dashboard design
Session 2
Face to Face Session 		8:30am – 12:30pm
Overview of Data Mining
  • Definition of data mining
  • Stages of data mining
  • Tools of data mining
  • Applications of data mining
 
12:30pm to 1:30pm
Lunch Break

1:30pm to 5:30pm
Data preparation and exploration
  • Identifying data quality issues
  • Data preparation for Data Mining
  • Data Mining Techniques
  • Association Analysis 
  • Clustering 
  • Predictive Modelling
Session 3
Face to Face Session 
8:30am – 12:30pm
    		Data Mining (DM) Applications for a Healthcare Provider
    • Identification of Re-Admission using Association Analysis
    • Identification of Effective Treatments using Clustering Analysis
    • Diagnosis of Disease using Predictive Modelling
     12:30pm to 1:30pmLunch Break
     1:30pm to 5:30pmData Mining (DM) Applications for a Non-Profit Organisation
    • Selection of Rehabilitation Programme using Association Analysis
    • Segmentation of Recidivism using Clustering Analysis
    • Prediction of Re-Integration Programme Outcomes using Predictive Modelling
    Session 4
    Face to Face Session     		 8:30am – 12:30pmData Mining (DM) Applications for Volunteerism
    • Evaluation of Volunteers using Association Analysis
    • Identification of Volunteer Churn using Clustering Analysis
    • Prediction of Training Programme Outcomes using Predictive Modelling
     12:30pm to 1:30pmLunch Break
     1:30pm to 5:30pm
    • Presentation
    • Recap of Data Mining Techniques
    • ECA Consultation

    Assessments

    • Assignments
    • Others
    • Class Participation

     

    About the Trainer

    Mr. Johnson Neo

    Mr. Johnson Neo is an experienced Adult Educator with over 12 years of expertise in teaching Data Visualisation, Data Analytics, and Machine Learning. He has designed and delivered numerous courses in these domains for tertiary institutions and corporate learners.

    He previously served as a Senior Lecturer at a local polytechnic and has been an Associate Faculty Member with the Singapore University of Social Sciences (SUSS) for more than a decade, where he continues to teach and mentor Full-Time students in applying data-driven insights for real-world problem solving.


      Course Fee

       International ParticipantsSingapore Citizens (below 40yrs), Permanent ResidentsSkillsFuture Mid-Career Enhanced Subsidy1 (S'poreans aged 40 and above)Enhanced Training Support for SME2 (Singaporean and PRs)
      Full Course fee (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 fee (A) - (B) = (C) $3,168.00$792.00$792.00$792.00
      9% GST on nett course fee (D)$285.12$71.28$71.28$71.28
      Total nett course fee payable, including GST (C) + (D) = (E)$3,453.12$863.28$863.28$863.28
      Less additional funding if eligible under various schemes (F)---$528.00
      Total nett course fee payable, including GST, after additional funding from the various funding schemes (E) - (F) = (H)$3,453.12$863.28$335.28$335.28

      Note:

      Singaporeans aged 40 years and above may apply to offset out-of-pocket course fees from  their SkillsFuture Credit (Mid-Career) 60 days before the programme start date, and up to 90 days after the programme start date. More information at /academics/executive-lifelong-learning/courses/grant---schemes/skillsfuture#skillsfuture-credit   

      For payment, please refer to /payment-modes for the various payment modes.

      For Refund Policy, please refer to /admissions/financial-matters/tuition-fee-subsidy/cet-courses#refund-policy.

      1. A written request for a refund must be submitted and is subject to approval. 
      1. If written notice of withdrawal is given within the cooling off period and before the course start date, a full refund of the fees paid less an administrative charge of $110.00 (exclusive of GST) will be given. 
      1. No refund will be given for withdrawal thereafter. 
      1. The cooling off period is defined as 7 working days after payment of course fee. 

       

      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

      For clarification, please contact the SUSS Academy via the following:

      Telephone: +65 6248 0263
      Email: [email protected]