Data Analytics and Visualisation Begins With Me

* Formerly known as Data Literacy Programme

Course Reference No:
TGS-2021004020 (Classroom & Asynchronous Learning)
TGS-2021002729 (Synchronous e-Learning)


Course Objectives

We live in a world that revolves around data. Data is regarded as the key resource that powers the world in the 21st century.

According to the International Data Corporation (IDC), many organisations still lack the data literacy skills to achieve business value. Organisation that have accumulated a large amount of data would not see much benefits until it is harnessed to work for your organisation.

Poor data literacy is one of the stumbling blocks and limits a company’s ability to reap value from data. To combat this, organisations will have to promote and develop their staff’s competencies in data.

Data literacy as defined by MIT is the ‘ability to read, work with, analyse and argue with data’. It is a set of core essential skills everyone in an organisation needs to use data effectively in their day-to-day activities and for decision-making. Used in the right way, data can help every employee achieve their objectives, perform their job better and contribute to overall company performance.

Employees who are data literate will also understand how to handle data appropriately, which will reduce the number of data breaches

Managers have to lead their organisations by setting the direction and the strategies on how data can be applied in the various parts of the organisation. 

This course is designed for all professionals to gain the fundamental skills to understand and work with data for the organisation. 

This course aims to provide participants with a foundation in:

  • Overview of data and data analytics
  • How to read, work, analyse and argue using data
  • Data as a language and common terminologies
  • Data analytics process and project life cycle
  • Tools and technology used for data analytics and visualisation
  • Machine learning and data visualisation techniques

At the end of the course, participants will be able to:

  • Apply data analytics techniques using tools through hands-on sessions
  • Apply data visualisation techniques using tools through hands-on sessions
  • Gain insights from data using various machine learning techniques
  • Work with and communicate using data


This programme consists of 2 components, Asynchronous e-Learning modules and Synchronous Online / Classroom Learning facilitated workshops as shown in the diagram below.

Asynchronous e-Learning modules consists of the following 8 modules:

  1. World of Big Data
  2. Data Literacy
  3. Data Fluency
  4. Data Analytics
  5. Land of Statistics
  6. Problem-solving with Data
  7. Arguing with Data
  8. Visualising Data

Three Synchronous Online / Classroom Learning facilitated workshops will cover the following topics:

Workshop 1
  • Analytics Project Life Cycle
  • Hands-on practice using Orange: Supervised learning models
Workshop 2
  • Hands-on practice using Tableau: Data Visualisation
Workshop 3
  • Hands-on practice using Orange: Classification Models and Unsupervised Learning Models


Orange and Tableau


Knowledge of fundamental statistics and mathematics

Who Should Attend

Professionals working with data

Mode of Training

Blended (On-campus/Online)


Dr. Amirhassan Monajemi


Amirhassan Monajemi is a Senior Lecturer in AI and Data Science with the School of Continuing and Lifelong Education (SCALE) at the National University of Singapore (NUS). Before joining the NUS, he was with the Faculty of Computer Engineering, University of Isfahan, Iran, where he was serving as a professor of AI and Machine Learning. He was born in 1968 in Isfahan, Iran. He studied towards BSc and MSc in Computer Engineering at Isfahan University of Technology (IUT), and Shiraz University respectively. He got his PhD in computer engineering, pattern recognition and image processing, from University of Bristol, Bristol, England, in 2005. His research interests include AI, Machine Vision, Data Science, and IoT.

He has taught the Data Science courses, including Data Mining, Advanced Data Mining, AI, and Neural Networks since 2008 at both undergraduate and postgraduate levels. He was awarded the best university teacher of the province in 2012. He also has studied Learning Management Systems, ELearning, and E-Learning for workplaces since 2007.

Dr. Monajemi was the data science advisor of MSC (Mobarakeh Steel Complex,the largest in the country). Moreover, he has registered three patents and published more than a hundred research papers in peer-reviewed, indexed journals and international conferences (IEEE, Elsevier, Springer, and so on), and supervised several Data Science, IoT, and AI industrial projects in various scales, including analysis of the effectiveness of research projects in enterprises, and data mining to extract the common patterns among the unsuccessful BSc students. He is experienced in different subdomains of data science from theory to practice, including data mining and pattern recognition.

Dr. Benjamin Lee


Benjamin Lee is a Senior lecturer in Data Analytics and Visualization with the School of Continuing and Lifelong Education (SCALE) at NUS. 

He has over 30 years’ experience in IT and information management. His career roles span CIO, Director of IT services, Strategy and Planning, Project management, Applications development, Systems engineering, Data management and IT outsourcing. Business and Industry experiences include electronic business development, implementation of business process re-engineering and functional support for Finance, HR, Manufacturing, Retail and mergers/acquisitions in Oil and Gas. 

He has been actively involved in data and information management since 2000 and has successfully implemented global projects covering data warehouses, management information systems, data analytics and e-business systems. In those projects, Ben had applied data analytics to improve resource management and sales profitability with better understanding of customers, products and services. 

Ben has been teaching information & data management and business analytics classes since 2016. He holds a Doctorate in Business Administration from the University of Western Australia. He also has Masters in Business Research and Physics and a Bachelors in Law. He has a strong interest and passion for data analytics, visualization and e-business. 

Ms. Chua Bee Luan


Chua Bee Luan is a Senior Lecturer in Data Analytics and Visualization with the School of Continuing and Lifelong Education (SCALE) at the National University of Singapore (NUS).

Bee Luan is a passionate educator with more than 14 years of teaching experience in an institute of higher learning. She taught modules in Business Analytics, Data Visualization, Website Design, Business Software, Statistical Methods and Calculus. In her capacity as a lecturer, Bee Luan was involved in developing FutureReady certification courses and conducting SkillsFuture for Digital Workplace courses for the adult learners in the banking and aviation industries. In support of the NUS resilience and growth (R&G) initiative, she also provides training for fresh graduates so that they can continue to learn in the uncertain times of COVID-19.

Prior to teaching, Bee Luan began her career in the IT industry in multiple roles, from project management to IT consulting in both MNC and SME companies. She had provided consulting services to her clients to streamline their business processes and had successfully rolled out numerous company-wide Enterprise Resource Planning (ERP) projects for companies mostly in trading and manufacturing industries.

Bee Luan graduated with a Bachelor of Science with Honours, double major in Computational Science and Mathematics from NUS. She also holds a Master in Business Administration in Strategic Management from the Nanyang Technological University and had obtained a Certificate in Teaching & Learning (Higher Education). Teaching has always been her passion and Data Analytics is her new area of interest.




1.5 Days
9.00am to 5.30pm (Daily)

Facilitated Learning : 10.5 hours
e-Learning               : 4.4. hours
Assessment             : 4.7 hours



International Participants


Incl. GST

Singapore Citizens
(39 yrs old or younger) 


Singapore PRs


Incl. GST

Singapore Citizens
(40 yrs or older)


Incl. GST

Enhanced Training Support for SMEs


Incl. GST

Sign Up Now

Fees & Funding

Singapore Citizen1
39 years old or younger
Singapore Citizen1
40 years or older eligible for MCES2
Singapore PRs Enhanced Training
Support for SMEs3
Full Programme Fee S$2,000.00 S$2,000.00 S$2,000.00 S$2,000.00 S$2,000.00
SSG Funding
(Refer to Funding Page for Claim Period)
- (S$1,400.00) (S$1,400.00) (S$1,400.00) (S$1,400.00)
Nett Programme Fee S$2,000.00 S$600.00 S$600.00 S$600.00 S$600.00
7% GST on Nett
Programme Fee
S$140.00 S$42.00 S$42.00 S$42.00 S$42.00
Total Nett Programme
Fee Payable, Incl. GST
S$2,140.00 S$642.00 S$642.00 S$642.00 S$642.00
Less Additional Funding if Eligible Under Various Scheme - - (S$400.00) - (S$400.00)
Total Nett Programme Fee, Incl. GST,
after additional funding from the various funding schemes
S$2,140.00 S$642.00 S$242.00 S$642.00

Learners must fulfill at least 75% attendance and pass all assessment components, to be eligible for SSG funding.

  1. All self-sponsored Singaporeans aged 25 and above can use their $500 SkillsFuture Credit to pay for the programme. Visit to select the programme. 
  2. Mid-Career Enhanced Subsidy (MCES) - Singaporeans aged 40 and above may enjoy subsidies up to 90% of the programme fee. 
  3. Enhanced Training Support for SMEs (ETSS) - SME-sponsored employees (Singaporean Citizens and PRs) may enjoy subsidies up to 90% of the programme fee. Click Here for more information. 
  4. Eligible organisations (excluding government entities) may apply for the absentee payroll funding for Singaporean/permanent resident participants attending the programme during working hours. The absentee payroll funding is computed at 80% of hourly basic salary capped at $4.50 per hour or $7.50 per hour for SME. For more information, visit Enterprise Portal for Jobs & Skills
  5. Eligible individuals may apply for training allowance capped at $6/hr under the WSS scheme, visit- WSG for more information..
  6. NTUC Training Fund (SEPs) – All self-employed (i.e. freelancers and sole-proprietors-with-no-employee) Singaporeans and Permanent Residents are eligible to apply for the NTUC Training Fund (SEPs) from NTUC’s Employment and Employability Institute (e2i). Click here for more information.
Sign Up Now
12 August 2021