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Short Course

Applied Analytics Using Predictive Modelling

Course ScheduleDuration (days)Mode of DeliveryTGS Reference Number
2 to 4 December 2025 | 9 am - 5.30 pm3Classroom LearningTGS-2025056121

Course Overview

Modern predictive analytics uses data to model a specific domain, isolate key factors and use the models or algorithms built using this process to predict likely future outcomes from new data. Predictive analytics is not a new concept and businesses have been using decision trees and regression to help correlate and classify their data and make predictions.

Mathematical modeling tools are applied to data in order to generate predictions about an unknown fact, characteristic, or even event not yet available in the dataset. To put it simply, we use statistical techniques to derive sophisticated predictive models and algorithms from large data sets without requiring explicit programming.

This course covers the life cycle of the data mining project and the key concepts of various predictive analytics techniques. It focuses on performing predictive analytics using supervised and unsupervised machine learning models on an open-source programming platform and how to communicate the results to audiences.

Course Objectives

Course will enable participants to:

  • Understand how to embark on a data mining project
  • Understand the fundamental key concepts of various predictive analytics techniques
  • Acquire basic proficiency on popular data analytics tools such as R
  • Understand how to prepare data for doing various predictive analytics techniques
  • Apply predictive analytics using popular tools
  • Identify which predictive analytics approaches to use for different situations

Who Should Attend

Any working professional who is interested to learn concepts, methodologies, and techniques in predictive analytics to improve business processes, and/or keen to use R open-source programming platform for predictive modeling.

Prerequisites

At least a polytechnic diploma. Participants are recommended to have knowledge of the fundamentals covered in Descriptive Analytics.

Technical Requirements

Participants should have a windows system that supports R packages and libraries for the practice labs.

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This programme is part of

  • Professional Certificate in Business Analytics

    • Types of Available Fundings

  • SkillsFuture Funding
  • SkillsFuture Credit
  • Enhanced Training Support
  • SFEC
  • PSEA

    • Testimonials


    Super excellent trainer! Dr Yeo is able to explain complicated technical terminology by using practical examples. It is indeed a pleasant and fruitful adult learning experience.

    Instructors


    Ms Samantha Sow

    Ms Samantha Sow

    Ms. Samantha Sow is currently a Senior Lecturer in the department of Information Systems and Analytics at the National University of Singapore (NUS). She has over 8+ years of experience in Business Analytics and Data Science and she conducts training for both government agencies and corporate clients. Before joining NUS, she lectures at Temasek Polytechnic, teaching Business Analytics and Data Science to professionals, managers and executives (PMEs). Prior to being an academia, Samantha worked in the research and development at Infineon Technologies. Her research and project areas include Business Analytics, Data Mining, Predictive and Prescriptive Analytics. She has a passion for engaging and inspiring participants to enhance their workplace analytics capabilities and increase business intelligence quotient within their organisations. Her interests lie in the applications of data analytics, predictive modelling and optimization techniques to derive actionable insights for commercial effectiveness. She is familiar with typical analytics tools such as Python, R, and SAS, SPSS and Tableau. She also has working knowledge in the area of Analytics, Data Science and Machine Learning. Samantha completed her Master of Education from University of Sheffield and graduated from the National University of Singapore with a Bachelor’s in Engineering, First Class Honours. She has also completed THEC (Teaching in Higher Education) and ACTA (Advanced Certificate in Training and Assessment). She is a member of the adult associate educator (AEN) by Institute for Adult Learning (IAL), Singapore. Her certifications include: Microsoft Certified Azure Data Scientist Associate. Microsoft Certified Data Analyst Associate. SAS Certified Predictive Modeller in SAS Enterprise Miner. Tableau Certified Desktop Specialist.

    Course Fees


    Singapore Citizens Singapore PRs Enhanced Training Support for SMEs International Participants
    39 years old
    or younger    		 40 years old
    or older
    Full Course Fee S$2,550.00 S$2,550.00 S$2,550.00 S$2,550.00 S$2,550.00
    9% GST on Full Course Fee S$229.50 S$229.50 S$229.50 S$229.50 S$229.50
    SSG Funding S$1,275.00 S$1,275.00 S$1,275.00 S$1,275.00 -
    Additional Funding if Eligible under Various Schemes - S$510.00 - S$510.00 -
    Total Nett Course Fee Payable, including GST S$1,504.50 S$994.50 S$1,504.50 S$994.50 S$2,779.50
    1. Total nett course fee payable, including GST, after additional funding from various funding schemes.
    2. Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
    3. The applicable funding is for Singaporeans and Singapore PRs. This is subject to change and in accordance with SSG funding guidelines as of course commencement.