Skip Ribbon Commands
Skip to main content

R for Data Science

Course Reference No:
TGS-2020513882 (Classroom Learning)

This course is aligned with the Infocomm Technology Skills Framework for Data Analyst and Data Scientist and will use R, a widely used statistical package. The course aims to quickly bring up to speed a programmer or business analyst who already knows how to programme in other language or have done advanced Excel macros to begin using R as a data science tool.

The course will define data science and explore the first two things a data scientist must do – cleaning and visualising data. You will learn and use R's dplyr, ggplot and ggvis packages for these tasks. It will then cover the Data Science Workflow – training models and testing them through the application of machine learning models to various industry-relevant data science problems. The tool used will be the Caret package.


Learning Objectives

At the end of the course, participants should have a working knowledge of how to solve data science problems with R and the following:

  • Use R for basic data munging to aggregate, clean and process data from local files and access databases and REST APIs
  • Create visualization with R/ggplot/ggvis
  • Create basic to intermediate analytics models with the R Caret package
  • Learn 12 common analytics models and how to programme and apply them in R

Course Outline

Day 1

  • Introduction to R: The R Ecosystem
  • Refresher: Basic R Programming
    • Data Types
    • Conditional Execution & Functions
  • The Analytics Process for Data Science
    • Data Acquisition (Read/Write Files)
    • Datasets & Packages
    • Data Cleaning & Transformation Pt 1
  • The Analytics Process for Data Science
    • Data Cleaning & Transformation Pt 2
    • Tidy Data Concepts/Application
  • Statistical Analysis with R
    • Summary Statistics
    • Correlation
    • Linear Regression/Multiple Linear
  • Regression
    • Hypothesis Testing (One-sided/Two sided T-test)
    • ANOVA

Day 2

  • Exploratory Data Analysis in R using Visualization
    • ggplot
    • rgl (3D Plots)
    • leaflet (Maps)
    • ggvis
  • Supervised vs Unsupervised Machine
  • Learning
  • Supervised Machine Learning in Caret
    • Classification
    • Cross Validation
  • Unsupervised Machine Learning using k-means clustering
    • Scree Plot
  • How to Build a Shiny App
    • User Interface (UI)
    • Server


Must be familiar with a progrmming language such as Java, C/C++ or Python and statistics 101 at pre-university level. Participants are required to bring along their personal internet laptop for the course.

Target Audience

Software Engineers, Programmers, Data Analysts


14 - 15 July 2022 FULL

22 - 23 September 2022 


9.00am to 5.30pm



Academia, L2-S1

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$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$570.00S$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$2,033.50 ​S$609.90 ​S$229.90 S$609.90 ​S$229.90


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


Dr Chia Hui Teng

Chia Hui Teng is a senior lecturer with the Department of Statistics and Data Science, National University of Singapore. 

Hui Teng is an experienced educator with close to two decades of teaching experience at institutions of higher learning.  Her area of expertise is in Statistics and Analytics education where she supports the upskilling of working adults and students in data competencies.  Hui Teng serves as an advisor in curriculum design and professional development, spearheaded various projects as well as conducted workshops in the area of statistics and visual analytics.  Her contributions in helping to prepare graduates for a data-centric workforce received national recognition.  She won the President's Award for Teachers, which is the highest accolade for the teaching profession in Singapore.

Hui Teng holds a PhD from the National Institute of Education, Nanyang Technological University.  Her research interests are in sense-making, visual analytics and statistical literacy. 

Dr Ning Yilin

Ning Yilin is a research fellow with the Centre for Quantitative Medicine, Duke-NUS Medical School. Her expertise is in the development of biostatistical and machine learning methods for healthcare applications. She has published research papers in top-tier journals, including Patterns (published by Cell Press) and eClinicalMedicine (published by The Lancet), and received the Khoo Postdoctoral Fellowship Award in 2021. As a researcher, Yilin is experienced in data analysis using R, and has published two R packages and one R Shiny application. She had teaching experience as a lecturer for an introductory course on R programing for medical students. Yilin holds a PhD from the NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore.


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 22 August 2022. For enquries, kindly contact

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 Hard copies are also available on request.