Data Analytics for IT Professionals

Course Reference No: CRS-N-0052790


Course Objectives

Data analytics will help organisations gain greater insights of their businesses to make better informed decisions and take actions. 

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

This course is designed for IT professionals to better understand data analytics and how to support and manage the technology and data.

This course aims to provide participants with a foundation in:

  • Overview of data and data analytics
  • 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


Day 1
  • Big Data and Data Analytics
  • Tools and Technology
  • Data Analytics Process
  • Introduction to Machine Learning – Supervised and Unsupervised Learning
Overview- All about Data 
  • Data concepts
  • Statistics Primer 
Analytics Project Life Cycle (CRISP-DM)
  • What is going with the data in each data analytics phase?
  • Model Development, Deployment, Scoring and Refresh
Hands-on Exercise Lab using Orange

Supervised Learning Models 
  • Data Cleaning Concepts/ Techniques
  • Exploratory Data Analysis
  • Data Preparation 
  • Linear Regression 
    • Model Evaluation R2
Hands-on Exercise Lab using Orange
  • Logistic Regression (Predicting HDB Prices)
  • Decision Tree, Random Forest
  • Model Evaluation 
    • Confusion Matrix, ROC, AUC

Day 2

  • Visualisation Concepts and Principles 
  • Selecting the Visualisations 
Hands-on Exercise Lab using Tableau 
  • Creating Visuals and Dashboards 
Unsupervised Learning Models 
Clustering Using K-means 
  • Concepts and Method 
  •  Application

Hands-on Exercise Lab Using Orange

NLP- Text Analytics 
  • Concepts and Method 
  • Application 
    • Text Pre-processing 
    • Visualisation 
    • Stop Words 
    • Clustering 
    • Text Classification 

Hands-on Exercise Lab Using Orange


Orange and Tableau


Knowledge of data management and fundamental statistics

Who Should Attend

Data and IT Professionals

Mode of Training



Dr. Benjamin Lee


Dr. 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.


Dr. Amirhassan Monajemi 


Dr. 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, E-Learning, 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 sub-domains of data science from theory to practice, including data mining and pattern recognition.





Ms. 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.




Mr. Koo Ping Shung is an experienced Data Scientist with an MBA degree and accumulated more than 15 years of relevant experience. He is a seasoned Trainer in Data Science as well, having trained numerous professionals on Analytics.

He was a guest lecturer at NUS-ISS, Adjunct Senior Faculty at SUSS and previously held an adjunct position in School of Information Systems, Singapore University of Social Science and ESSEC Singapore,. He is currently the mentor to the trainees accepted into the IMDA-SAS BIA Programme for 10 intakes.

Prior to this, he was the Analytics Practicum Manager of the Master of IT in Business (Analytics) Singapore Management University, School of Information Systems. He managed the industrial relationships through projects and attachments. He often advised companies on the type of Analytics projects they can do with their data and is a co-supervisor for many MITB students on their Analytics Capstone Projects.

During his time at SMU, he was an instructor in the DBS Graduate Associate Programme (GAP) for 3 years, teaching over 200 GAs on what Analytics is all about, and received positive ratings. He has trained numerous professionals on Analytics and usage of SAS software from various companies as well, from industry like financial services, pharmaceutical and logistics.

Besides being an instructor for DBS GAP, he was a facilitator for IDA Data Science MOOC programme for 2 cohorts (over 300 professionals) and participated in Singapore's first Data Literacy Bootcamp, co-organised by the IDA Singapore and The World Bank.

Before going to SMU, Ping Shung was formerly from the banking industry, having worked in DBS Bank and OCBC Bank, specialized in Credit Risk Analytics and also maintained data mart and its processes for the department. His banking experience has equipped him with knowledge on how data, IT infrastructure, compliance and regulations affect Analytics and vice versa. Ping Shung also has experience in implementing mathematical models onto IT systems.

Ping Shung through his career has gathered much experience on statistical modeling, from working in the banks, supervising MITB (Analytics) students and doing education research. His Analytics experience range from a wide variety of business functions and industries, gathered from talking to companies from a wide variety of industries.

Ping Shung’s strong passion in Data Science & Artificial Intelligence can be seen through his involvement in Analytics tech communities, being a Co-founder of DataScience.Sg, former Working Committee Chairman of SAS User Group Singapore and Data Ambassador for one of DataKind SG project and workshop facilitator. He also read widely on the different topics related to Data Science and Artificial Intelligence, keeping himself up-to-date with their development. He has presented to organizations such as NLB, BCA, CPF, IRAS and SEMI. He also contributes articles to AI Singapore, an institution in Singapore, focused on building AI capabilities.

His research interest lies in how Data Science/Artificial Intelligence can help organizations and businesses to be more efficient and effective, and how organizations can build in-house data science capabilities successfully.

Ping Shung holds an MBA from University of Adelaide. He obtained his bachelor degree in Economics from National University of Singapore, with a minor in Computational Finance.


27 - 28 Oct 2020(Online – FULL)
19 - 20 Nov 2020(Online – FULL)
14 - 15 Dec 2020 (Online – FULL)


2 Days
9.00am to 5.30pm 


National University of Singapore
University Town

International Participants


Incl. GST

Singapore Citizens (39 yrs old or younger) or Singapore PRs


Incl. GST

Singapore Citizens(40 yrs or older)


Incl. GST

Enhanced Training Support for SMEs


Incl. GST

Fees & Fundings

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$1,900.00 S$1,900.00 S$1,900.00 S$1,900.00 S$1,900.00
SkillsFuture Funding
(Refer to Funding Page for Claim Period)
- (S$1,330.00) (S$1,330.00) (S$1,330.00) (S$1,330.00)
Nett Programme Fee S$1,900.00 S$570.00 S$570.00 S$570.00 S$570.00
7% GST on Nett
Programme Fee
S$133.00 S$39.90 S$39.90 S$39.90 S$39.90
Total Nett Programme
Fee Payable, Incl. GST
S$2,033.00 S$609.90 S$609.90 S$609.90 S$609.90
Less Additional Funding if
Eligible Under Various Scheme
- - (S$380.00) - (S$380.00)
Total Nett Programme Fee, Incl. GST,
after additional funding from the various funding schemes
S$2,033.00 S$609.90 S$229.90 S$609.90

  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. For more information, visit
  4. Eligible organisations (excluding government entities) may apply for the absentee payroll funding via SkillsConnect at 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
  5. Eligible individuals may apply for training allowance capped at $6/hr under the WSS scheme, visit- for more information on WSS.
  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
09 October 2020