Data Analytics and Visualization for Managers

Course Reference No:
CRS-N-0052731 (Classroom Learning)
CRS-N-0046240 (Synchronous e-Learning)


Course Objectives

This course provides an exploratory tour of big data, data analytics, data science, machine learning and artificial intelligence. It introduces the various tools and techniques used by data science teams today. A roadmap of how to build an internal data science competency in an organisation will be discussed. The course provides exposure to building static and interactive graphics using Tableau and Orange including a demonstration on the building of actual analytics models in Orange.

At the end of this course, participants should be able to;

  • Understand what is Big Data, Data Science/Analytics, Machine Learning and Artificial Intelligence
  • Describe and manage the data analytics process and workflow
  • Understand the various tools used for analysis and data platforms required to support the analytics process
  • Identify the competencies required to build a data science team
  • Understand the tools for visualization and building data science models


Day 1
  • Painting the big picture – Big Data and Data Analytics
  • The Data Analytics Process
  • Introduction to Machine Learning
  • A Primer on Artificial Intelligence
  • Introduction to Statistical Concepts
Hands-on session
  • Data Cleaning Concepts/Techniques
  • Analytics with Orange Workshop
  • Exploratory Data Analysis and Linear Regression-Predicting HDB Prices
  • Logistic Regression-Predicting HDB Prices

Day 2
  • Stages in Analytics Projects
  • Open Source and Innovation
  • Building the Data Science Team
  • COE for Analytics
  • Privacy, Ethics and Biases
  • Project Scoping

 Hands-on session
  • Analytics with Orange Workshop (Credit Card Fraud Detection)
  • Sentiment Analysis of Survey Data

Day 3
Introduction to Data Visualization:
  • Why do visualization
  • Data Visualization Principles: Visual Components
  • Data Visualization Technique and Design: Types of Graphs & Insights
Communicating with Data Visualizations
  • Data Storytelling with Dashboards
Hands-on session (Tableau Part 1)
  • Connect to Your Data
  • Data Cleaning: Preparing Excel Files for Tableau
  • Setting Up Data Sources: Data Joins
Hands-on session (Tableau Part 2)
  • Drill Downs, Hierarchies, Filtering
  • Creating Visualizations/ Maps/Dashboards/Stories
  • Creating Filter/Highlight Action
  • Group Project: US Cities, Attracting Investment Capital

Hands-on session (Tableau Part 3)
  • Create a Preliminary Narrative/ Improve Narrative via Exploratory Data Analysis
  • Communicate results in a Presentation


Orange, Tableau


The participant should have an IT/software project development, architecture or management experience. Some programming experience and statistics at a pre-university level is required to participate in this programme. An understanding of common IT software, databases, programming tools and environments is preferred.  

Who Should Attend*

Business analyst, customer service manager, physiotherapist, procurement officer, HR executive and any professional, manager and executives who needs to use data to tell a story.

*Note: This course is suitable for participants who have not attended any of the following courses: 

If the participant has attended any one of the above courses, he / she is encouraged to attend the other two one-day courses to achieve similar outcome.

Mode of Training

On-campus or Online (Live)


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.


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 Julian Lin

Dr Julian Lin is a Senior Lecturer in Cybersecurity and Data Analytics with School of Continuing and Lifelong Education (SCALE) at the National University of Singapore (NUS).  He is a Certified Information Systems Security Professional (CISSP) and has a dozen other IT certifications. Recently, Julian was ranked 6th in the Microsoft Data Science Capstone Competition. He has been conducting text-analytics research since 2010 and teaching visualization since 2007. 

During his IT consultancy career, he oversaw the application and infrastructure projects in Amoseas, Mannesmann Dematic Colby (Sydney), Alcatel (Sydney), and the University of New South Wales (UNSW) Faculty of Commerce and Economics. Julian has taught programming and data management for business at UNSW.  At NUS, he had mentored student software development projects and taught visual communications, designing new media content, and research methods. He was given a teaching award for visualization class and research award for user acceptance research in NUS. 

Julian obtained a PhD degree in Information Systems from NUS.  He was a recipient of two scholarships from the Overseas Chinese Association in Taiwan while pursuing his Bachelor degree and another scholarship while pursuing a Master degree in Australia.

Ms Chua Bee Luan

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.


Dr Edmund Low

Dr Edmund Low is a lecturer with the University Scholars Programme (USP) at the National University of Singapore. He teaches courses on engineering, statistical methods, data science and analytics. He currently heads the quantitative reasoning domain, and is also director of the Quantitative Reasoning Centre, at USP. He has organised / co-organised programming workshops and data hackathon for students. As an educator, Edmund has received both the USP Teaching Excellence Award, as well as the NUS Annual Teaching Excellence Award. He has more than 13 years of academic and professional experience in the use of computational modelling and data-driven tools, applying them to solve problems in public health, water resource management and air quality in buildings. Edmund holds a PhD in Environmental Engineering from Yale University.


Dr Guo Lei

Dr Guo Lei is an active educator and researcher in data science, behavioural study and design thinking, with extensive experience in delivering practice-based learning programmes and applied research projects with successful results.

Starting her career in Singapore as a marketing practitioner, Dr Guo worked across manufacturing, entertainment and education industries. She was the Chief Representative in China for a Singapore listed company. She was also responsible for setting up Shanghai Office and promoting executive education programmes for NUS Business School in Greater China market.

With the aspiration of bridging the gap between research and practice, Dr Guo pursued her PhD in the UK, where she worked on large-scale research projects with Cambridge University Service Alliance, BAE Systems and China Mobile.

Dr Guo returned to Singapore and joined NUS as a faculty member in 2011. She has particular experience in tackling complex challenges through applied research and education. She was the Principal Investigator for a series of research projects to inform better public transport policy decisions. She consults local and overseas corporate clients on data analytics, user experience design and service innovation. Dr Guo teaches executive programmes of Data Analytics and Design Thinking at NUS. She has a passion for engaging and inspiring working professionals at all levels by applying the theory to real world business applications.

Dr Guo holds a PhD in Marketing from University of Exeter, an MBA from University of Adelaide and a BA in Literature from Beijing Normal University.


Mr Koo Ping Shung

Mr Koo Ping Shung is an experienced Data Scientist with more than 13 years of relevant experience. He is also currently a Adjunct Senior Faculty with the Singapore University of Social Sciences (SUSS) and a SAS Trainer as well. To date, he has conducted over 1,600 man-hours of data science training. He is also the mentor to the trainees accepted into the IMDA-SAS BIA Programme for 5 intakes.

Ping Shung was a guest lecturer at NUS Institute of Systems Science and previously held an adjunct position in School of Information Systems, Singapore Management University.

Prior to this, he was the Analytics Practicum Manager of the Master of IT in Business (Analytics) at the Singapore Management University School of Information Systems. He managed the industrial relationships through projects and attachments. He often advised companies on the type of data analytics projects they can work on with their data and is a co-supervisor for many Masters students on their data analytics capstone projects. He was an instructor for the DBS Graduate Associate Programme for 3 years, teaching over 200 Graduate Associates on data analytics and received positive ratings. He has also trained professionals from various companies on data analytics and the use of SAS software.

Ping Shung was a facilitator for the IDA Data Science MOOC programme for 2 cohorts (over 300 professionals) and participated in Singapore's first Data Literacy Bootcamp which was co-organised by the IDA Singapore and The World Bank.

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

His strong passion in data analytics and data science can be seen through his involvement in data analytics interest groups, being a Co-founder of DataScience.Sg and former Working Committee Chairman of SAS User Group Singapore and Data Ambassador for one of the DataKind SG project. He also read widely on the different topics related to data science and artificial intelligence, keeping himself up-to-date with their development.

His research interest lies in how data science can help organisations and businesses to be more efficient and effective.

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.


09 -11 Nov 2020(Online – FULL)
17 - 19 Nov 2020 (Online – FULL)
23 - 25 Nov 2020 (Online – FULL)
30 Nov – 02 Dec 2020 (Online – FULL)
02 - 04 Dec 2020(Online – FULL)
07 - 09 Dec 2020(FTF– FULL)
16 - 18 Dec 2020 (Online – FULL)
15,18,19 Jan 2021 (FTF)


3 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$2,850.00 S$2,850.00 S$2,850.00 S$2,850.00 S$2,850.00
SkillsFuture Funding
(Refer to Funding Page for Claim Period)
- (S$1,995.00) (S$1,995.00) (S$1,995.00) (S$1,995.00)
Nett Programme Fee S$2,850.00 S$855.00 S$855.00 S$855.00 S$855.00
7% GST on Nett
Programme Fee
S$199.50 S$59.85 S$59.85 S$59.85 S$59.85
Total Nett Programme
Fee Payable, Incl. GST
S$3,049.50 S$914.85 S$914.85 S$914.85 S$914.85
Less Additional Funding if
Eligible Under Various Scheme
- - (S$570.00) - (S$570.00)
Total Nett Programme Fee, Incl. GST,
after additional funding from the various funding schemes
S$3,049.50 S$914.85 S$344.85 S$914.85

  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.
NTUC members enjoy 50% of the unfunded course fee support for up to $250 each year for courses supported under UTAP (Union Training Assistance Programme). Terms and Conditions apply. Please visit
Sign Up Now
05 November 2020