WSQ Big Data Analytics for Professionals, Managers and Executives

This course equips participants with the knowledge and skills required in big data analytics domain to solve business problem and improve the system/process.  Preparing learners to be the big data analytics professionals, it empowers the learners with practical skills and underlying knowledge and abilities to integrate the use of data analytics in the production environment for the identification of bottlenecks and system improvements, define the hypotheses for the business problem, select the Big Data technologies and tools to be implemented in an organization based on the data requirements. 


Big data analytics is a process of examining big data to uncover information such as hidden patterns, correlations, market trends and customer preferences that can help organizations make informed business decisions.  Organizations can use big data analytics systems and software to make data-driven decisions that can improve business-related outcomes. The benefits may include more effective marketing, new revenue opportunities, customer personalization and improved operational efficiency. With an effective strategy, these benefits can provide competitive advantages over rivals. 

Programme Objective

At the end of the programme, learners will be able to: 


  • Interpret business issues and formulate the hypotheses through appropriate data analytics tools and techniques.  
  • Appraise applicable Big Data technologies and tools through investigating the pros & cons, components and types of frameworks. 
  • Design and implement business-based solutions according to data requirements and data analytics plan. 

Programme Outline

1. Introduction of Big Data Analytics 


  • Industry 4.0 
  • Big Data Analytics 
  • Use Cases / Applications 
  • Benefits and Challenges 
  • Business Case, Problem Statement & Hypothesis Formulation 
  • Tools and Techniques of Data Analysis Process 


2. Components and frameworks of big data technologies and tools 


  • Big Data Architecture 
  • Layers 
  • Processes 
  • Tools used in Big Data Analytics  
  • Data storage and management 
  • Data cleansing 
  • Data analysis 
  • Data visualization 
  • Hadoop Ecosystem 
  • Data Requirements 
  • Data Analytics Plan 
  • Data Visualization Principles 
  • Types of Graphs & Insights 
  • Data Storytelling with Dashboards 


3. Design and drive the solution based on the business problem and hypotheses 


  • Data Requirements 
  • Data Analytics Plan 
  • Data Visualization Principles 
  • Types of Graphs & Insights 
  • Data Storytelling with Dashboards 
  • Briefing & preparation of Hands-on-Practical Session (Visualization) 
  • Hands-on-Practical Session (Visualization): Implementation of data visualization using Microsoft Power BI 
  • Introduction of Machine Learning 
  • Use cases / applications 
  • Tools & technique of Machine Learning 
  • Briefing & Preparation of Hands-on-Practical Session (Machine Learning) 
  • Hands-on-Practical Session (Machine Learning): Implementation of regression prediction model using Microsoft Azure Machine Learning Studio (classic) 

This course adheres to the SSG Skills Framework <Data Analytics System Design-ELE-ACE-5001-1.1> . Trainees who attended at least 75% of the scheduled class, demonstrated competency in the WSQ assessment, and participated during the scheduled class will receive these two e-certificates with the aforementioned skills under the SSG Skills Framework.

Programme Fee

*Course fees before GST

**Funding is subjected to approval

Note that purchases of goods and services from GST-registered businesses will be subject to GST at 9% GST. The GST amount calculated will be based on full course fees.


It is recommended that participant meet the following: 


  • Minimum age requirement: 21 years old. 
  • Able to speak, listen, write, and read English with Numeracy skills at a minimum proficiency level 4 of the Employability Skills System (i.e., Workplace Literacy and Numeracy, WPLN). 
  • Possess Secondary School educational level qualification (O or N-Level). 
  • Minimum knowledge of data analytics required. 
  • At least 2 years relevant supervisory or managerial work experience. 

Terms & Conditions

1. All notice of transfer/withdrawal / deferment must be given in writing and submitted at least 2 weeks prior to course commencement. 


2. An administrative fee of $60 (GST inclusive) will be imposed if notice is received less than 2 weeks. 


3. If notice of withdrawal is received: 

  • At least 1 week before commencement of the course, a 20% of the full course fee will be charged. For government-funded course, a 20% of full course fee before funding will be charged. 
  • Less than 1 week before commencement of the course, a 30% of the full course fee will be charged. For government- funded course, a 30% of full course fee before funding will be charged. 
  • No show on the scheduled date, a full course fee will be levied. For government-funded course, a full course fee before funding will be charged. 


3. For all government-funded programmes (WSQ & Non-WSQ), funding is only applicable to: 

  • Singapore Citizens or Singapore Permanent Residents. 
  • Participants who have achieved at least 75% attendance and passed all required assessments. 
  • Full course fee will be charged to participants who fail to meet the above-mentioned criteria. 


4. Certificates or Statement of Attainment or Certificate of Attendance will only be issued to participants who have achieved 75% attendance and undergo assessment (if applicable). 


5. When a course is cancelled, fails to commence or fails to complete under unforeseen circumstances, participant is allowed to defer the intake at no cost or withdraw from the course; under such situation, a full refund of the advance payment will be given. 


6. Notice of change in participant’s name must be given in writing, no less than 5 days prior to course commencement. 


7. SMF reserves the right to change the venue, cancel or postpone the event without prior notice and full refund will be given under such circumstances. Such modifications shall become effective immediately upon the posting thereof. Please approach your account manager for more queries. 


8. SMF Centre for Corporate Learning Pte Ltd has a Data Protection Policy which provides more information about how we collect, use and disclose your personal data. Please click the link below to know more.

Appeal Process

1. The candidate has the right to disagree with the assessment decision made by the assessor.


2. When giving feedback to the candidate, the assessor must check with the candidate if he agrees with the assessment outcome.


3. If the candidate agrees with the assessment outcome, the assessor & the candidate must sign the Assessment Summary Record.


4. If the candidate disagrees with the assessment outcome, he/she should not sign in the Assessment Summary Record.


5. If the candidate intends to appeal the decision, he/she should first discuss the matter with the assessor/assessment manager.


6. If the candidate is still not satisfied with the decision, the candidate must notify the assessor of the decision to appeal. The assessor will reflect the candidate’s intention in the Feedback Section of the Assessment Summary Record.


7. The assessor will notify the assessor manager about the candidate’s intention to lodge an appeal.


8. The candidate must lodge the appeal within 7 days, giving reasons for appeal together with the appeal fee of $108.00 (inclusive of 8% GST).


9. The assessor can help the candidate with writing and lodging the appeal.


10. The assessment manager will collect information from the candidate & assessor and give a final decision.


11. A record of the appeal and any subsequent actions and findings will be made.


12. An Assessment Appeal Panel will be formed to review and give a decision.


13. The outcome of the appeal will be made known to the candidate within 2 weeks from the date the appeal was lodged.


14. The decision of the Assessment Appeal Panel is final and no further appeal will be entertained.


15. Please click the link below to fill up the Candidates Appeal Form.

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Programme Key Information