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Data Analytics Begins With Me

Course Reference No:
TGS-2020501963 (Synchronous e-Learning)

Basic Data Analytics taught with a simple but powerful graphical user interface (“GUI”) drag-and-drop tool targeted at business analysts requiring more sophisticated capabilities than what a spreadsheet can offer.

Understanding and using data is increasingly an important part of an executive function. Whether you are a business analyst, customer service officer, physiotherapist, procurement officer or HR executive – the ability to understand where potential data is coming from, and to properly collect, clean, and then analyse it, will be an important and critical skill.

This course provides the participant with the knowledge and skills to participate in the organisation’s data analytics process without any coding requirements. We will cover basic concepts of data analysis, processing of data, and visualisation of raw data and processed data to gain insights, followed by building simple models for classification and prediction.

The participant will also learn to use the free and open source data analytics tool – Orange – a powerful and popular Python-based data analytics tool. Orange has a point-click-drag-and-drop GUI, no programming is required.

 

Learning Objectives

At the end of the course, participants will be able to:
  • Understand the data analytics end-to-end workflow
  • Identify where the potential sources of data can come from
  • Use a GUI-based analytics tool to process, clean and prepare data for analysis
  • Perform basic analysis such as classification and predictions
  • Select the most appropriate analytics model to use for a specific task
  •      

Course Outline

This course will allow you to gain knowledge in using the data analytics tool, Orange
  • 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
    • Data Cleaning Concepts/Techniques
    • Analytics with Orange Workshop
    • Exploratory Data Analysis and Linear Regression-Predicting HDB Prices
  • Logistic Regression-Predicting HDB Prices

  

Pre-requisites

Participants should have statistical and mathematics background as well as experience with using Excel in manipulating data, charting and basic analysis. No programming experience required.
    

Target Audience

SingHealth staff, especially analysts and employees, who are using Data Analytics in their course of work.

 

Date

29 July 2022, Friday FULL 

26 August 2022, Friday


Time

9.00am to 5.30pm

 

Delivery Mode

Synchronous e-Learning via Zoom

Course Fees

International Participants

Singapore Citizen
39 years old or younger

Singapore Citizen
40 years or older eligible for MCES
Singapore PRsEnhanced Training Support for SMEs
​Full Programme Fee​S$850.00​S$850.00​S$850.00​S$850.00​S$850.00
​SkillsFuture Funding (Refer to Funding Page for Claim Period) ​-​(S$595.00)​​(S$595.00)​​(S$595.00)​​(S$595.00)
​Nett Programme Fee​S$850.00​S$255.00S$255.00​S$255.00​​S$255.00
​7% GST on Nett Programme Fee ​S$59.50​S$17.85​​S$17.85​​S$17.85​​S$17.85
Total Nett Programme Fee Payable, Incl. GST​​S$909.50​S$272.85​​S$272.85​S$272.85​​​S$272.85
​Less Additional Funding if Eligible Under Various Scheme-​-​​(S$170.00)​-​(S$170.00)
Total Nett Programme Fee, Incl. GST, after additional funding from the various funding schemes ​​S$909.50​S$272.85​S$102.85​S$272.85​S$102.85​
   

 

Organisers

  • NUS School of Continuing and Lifelong Education (SCALE)
  • NUS Faculty of Science
  • SingHealth Health Services Research Centre
  • SingHealth Academy      

 

Trainers

Dr Benjamin Lee

Dr Lee is a Senior lecturer in Data Analytics and Visualisation with the Faculty of Science 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, Dr Lee 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, visualisation and e-business.

Mr Alwin Zhang

Alwin Zhang is a Research Analyst (Data Science) currently with the department of Health Services Research Center (HSRC) in Singhealth, with a joint appointment of a Research Associate in DukeNUS. He graduated from NUS with a Masters in Business Analytics since 2018. He has a strong research interest towards applied data analytics, harnessing open-source machine learning and deep learning methodologies to solve issues and ease workflows within the healthcare industry. He has previously conducted various workshops including Introduction to R programming and Data de-identification within Singhealth.

 
 

Registration Details

NOTE: Please ensure that you submit the training request form to your respective HR departments for approval before sending in your registration.

Click here to register by 29 Jun 2022. For enquries, kindly contact hsr@singhealth.com.sg.   

Registration is on a first-come, first-served basis. Successful registrants will be notified via email with more information.

   
In line with the Singapore Personal Data Protection Act (PDPA), please note that we have updated our SingHealth Data Protection Policy, a copy of which is available at http://www.singhealth.com.sg/pdpa. Hard copies are also available on request.