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Professional Certificate

Professional Certificate in Business Analytics

Programme Structure


Programme NameProgramme ScheduleMode of DeliveryProgramme Code
Descriptive AnalyticsTo be advisedClassroom LearningTGS-2019507052
Applied Analytics Using Predictive ModellingTo be advisedClassroom LearningTGS-2019507053
Prescriptive Analytics – From Data to Optimal Course of ActionTo be advisedClassroom LearningTGS-2022015667
Duration 7 days

Programme Overview

This Professional Certificate in Business Analytics programme empowers learners with essential competencies in business analytics, enabling them to effectively identify, analyse, and solve business challenges using data-driven insights. Learners will develop expertise in descriptive statistics, numerical summaries and visual data representation, as well as designing and evaluating algorithms to inform organizational decisions, through descriptive, predictive, and prescriptive analytics. By mastering fundamental methods, frameworks, and techniques in analytics, learners will be able to extract actionable intelligence from data, facilitating informed business decisions. Additionally, the programme ensures proficiency in utilising advanced business analytics tools within organisational contexts, promoting a strategic approach to leveraging data for competitive advantage.

Programme Objectives

This Professional Certificate will equip learners with the following competencies:

  • Identify, analyse and solve critical business problems using analytics
  • Design and evaluate algorithms to drive organisational decisions and insights using descriptive, predictive and prescriptive analytics
  • Understand and apply fundamental methods, frameworks and techniques of analytics to make sense of data and make informed business decisions
  • Acquire competency in using business analytics tools in their organisations

Job Role Readiness

This Professional Certificate will prepare learners in the following job roles to perform their responsibilities more effectively:

  • Data Analysts/Associate Data Engineer

Who Should Attend

Professionals who are keen to explore work opportunities in analytics.

Prerequisites

At least a polytechnic diploma.

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Types of Available Fundings

  • Enhanced Training Support
  • SFEC
  • PSEA

    • 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.

    Dr Yeo Wee Kiang

    Dr Yeo Wee Kiang

    Dr Yeo Wee Kiang is a Senior Lecturer at the Department of Information Systems and Analytics in the NUS School of Computing, where he is dedicated to researching the use of technologies like Adaptive Learning and Large Language Models (LLMs) to create personalized and engaging computing education experiences. With a PhD from NUS focused on machine learning and data mining for pharmaceutical drug discovery, Dr Yeo brings practical industry experience as the former Director of Data Science at a food tech startup, where he led a team applying AI and machine learning to enhance nutritional value. His diverse background includes being an Adjunct Lecturer in the Department of Economics at NUS Faculty of Arts and Social Sciences, helming the Big Data Analytics module in the Master of Economics programme from 2019 to 2023, and teaching Python programming and data analytics at Nanyang Business School. Prior to his academic appointments, Dr Yeo was the Lead Instructor for Data Science at a New York-based commercial Continuing Education and Training (CET) provider, teaching aspiring adult learners in Hong Kong, Sydney, and Singapore. He also holds the WSQ Advanced Certificate in Training and Assessment (ACTA) from the Institute for Adult Learning Singapore. As part of the NUS ACE (Advanced Computing for Executives) teaching team, Dr Yeo specializes in technical training and practical computing education focused on data analytics, machine learning, and Python, aiming to equip professionals with in-demand Data Science skills.

    Programme Fees


    Singapore Citizens Singapore PRs Enhanced Training Support for SMEs International Participants
    39 years old or younger 40 years old or older
    S$1,945.65 S$755.65 S$1,945.65 S$755.65 S$6,485.50
    1. Total Nett Programme Fee Payable, including GST, after additional funding from various funding schemes.
    2. Participants must fulfill at least 75% attendance and pass all assessment components for all short courses within a professional certificate in order to be eligible for SSG funding.
    3. Please note that all external funding for courses is limited in duration and subject to eligibility and availability.

    Advanced Computing for Executives

    11 Research Link
    COM 3, #B1-07
    Singapore 119391
    Email: soc-ace@nus.edu.sg

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