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Short Course

Relational Database Design

Course ScheduleDuration (days)Mode of DeliveryTGS Reference Number
18 and 19 November 2025 | 9 am - 5.30 pm2Classroom LearningTGS-2025057679

Course Overview

This course teaches relational database design. It covers how to abstract information into a data model, how to normalise the data model so that the database is free of anomalies, and how to transform the final, normalized data model into a relational database design, ready to be implemented.

Course Objectives

This course will equip learners with the following competencies:

  • Learn what is data and big data
  • Learn how to design a relational database
  • Learn to design a database from using data models
  • Learn what normalisation is, and how to ensure database is free of anomalies
  • Learn each of the three normal forms in Database Normalization
  • Learn how to identify tables, and how to create relationships

Job Role Readiness

Data Analysts and Business Analysts.

Prerequisites

At least a polytechnic diploma.

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This programme is part of

  • Professional Certificate in Database Management

    • Types of Available Fundings

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

    • Testimonials


    I'm a fullstack developer and already have a working understanding of databases. I'm already familiar with most of the content, but the course goes more in-depth and enhanced my understanding of what I currently know. It's useful to plug in the gaps in small implementation details to become a better developer and also helps in communication with other developers as I'm introduced to the nomenclature for each concept I'm applying. I find the tools introduced useful as well since I can use them immediately in my work.

    Excellent teaching from experienced instructor! Definitely recommended!

    Excellent and professional trainer.

    Instructors


    A/Prof Danny Poo

    A/Prof Danny Poo

    A/Prof Danny Poo brings with him 35 years of Software Engineering and Information Technology and Management experience. A graduate from the University of Manchester Institute of Science and Technology (UMIST), England, Dr Poo is currently an Associate Professor at the Department of Information Systems and Analytics, National University of Singapore. Prior to joining the University, Dr Poo was with the System Operations at DBSBank, Singapore. A Steering Committee member of the Asia-Pacific Software Engineering Conference, Dr Poo is actively involved in Information Management and Healthcare Analytics research. A well-known speaker in seminars, Dr Poo has conducted numerous in-house training and consultancy for organizations, both locally and regionally. Dr Poo is the author of five books on Object-Oriented Software Engineering, Java Programming language and Enterprise JavaBeans.

    Dr Poo notable teaching credentials include:

    • Data Strategy
    • Data StoryTelling
    • Data Visualisation
    • Data Analytics
    • Machine Learning
    • Data Management
    • Data Governance
    • Data Architecture
    • Capstone Projects for Business Analytics
    • Software Engineering
    • Server-side Systems Design and Development
    • Information Technology Project Management
    • Health Informatics
    • Healthcare Analytics
    • Health Informatics Leadership.
    Industry Credentials
    • Deutsche Bank
    • Gemplus
    • Micron
    • NCR
    • PIL
    • PSA
    • Rhode-Schwarz
    • Standard Chartered Bank
    • ST Electronic
    • Monetary Authority of Singapore
    • Infocomm Development Authority
    • National Library Board
    • Ministry of Manpower
    • Nanyang Technological University
    • Nanyang Polytechnic
    • National University Hospital.

    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$1,900.00 S$1,900.00 S$1,900.00 S$1,900.00 S$1,900.00
    9% GST on Full Course Fee S$171.00 S$171.00 S$171.00 S$171.00 S$171.00
    SSG Funding S$950.00 S$950.00 S$950.00 S$950.00 -
    Additional Funding if Eligible under Various Schemes - S$380.00 - S$380.00 -
    Total Nett Course Fee Payable, including GST S$1,121.00 S$741.00 S$1,121.00 S$741.00 S$2,071.00
    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.