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SCTP Course

(SCTP) Applied Analytics Using Predictive Modelling

Course ScheduleDuration (days)Mode of DeliveryTGS Reference Number
27, 31 July and 3 August 2026 | 9 am - 5.30 pm3Classroom LearningTGS-2024046918
This course is part of the (SCTP) NUS Data Intelligence and Business Analytics Programme. Applicants are required to register for all eight SCTP courses.

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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Funding Available

  • SkillsFuture Funding
  • SkillsFuture Credit

    • 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


    Dr Prakash Chandra Sukhwal

    Dr Prakash Chandra Sukhwal

    Prakash Sukhwal is a Senior Lecturer in the Department of Information Systems and Analytics at National University of Singapore. He obtained his Ph.D. in Information Systems and Analytics from School of Computing in 2024. Prior to his current role, he was a Principal Data Scientist in manufacturing industry, where he utilised his machine learning and GenAI skills to drive innovation. With over nine years of teaching experience, Prakash has taught extensively to both university graduates and industry professionals, covering courses from basic to advanced analytics. His research interests include the use of large language models, health informatics, knowledge graphs, and conversational AI. He has published in top-tier journal and conferences, such as PNAS, ICIS, HICSS, PACIS, and AAAI, and has received the NUS Research Achievement Award twice, in 2020 and 2022.

    Course Fees


    Singapore Citizens Singapore PRs
    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
    Less: SSG Grant Amount S$1,785.00 S$1,785.00 S$1,785.00
    Nett Course Fee S$765.00 S$765.00 S$765.00
    9% GST on Nett Course Fee S$68.85 S$68.85 S$68.85
    Total Nett Course Fee Payable, including GST S$833.85 S$833.85 S$833.85
    Less Additional Funding - S$510.00 -
    Total Nett Course Fee Payable, including GST, after additional funding from the various funding schemes S$833.85 S$323.85 S$833.85
    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.
    4. Please refer to our Terms and Conditions page for more information.

    Other Information

    The SkillsFuture Career Transition Programme (SCTP) helps mid-career individuals build industry-relevant skills to boost employability and transition into new sectors or roles. Interested in learning more about the SCTP? Explore the FAQ to find answers to common questions, including programme details, eligibility criteria and funding.