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

Pre-requisites

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

Date

27 and 28 July 2023

Time

9.00am to 5.30pm

Venue

Location for 27th July 2023 is L1-S2, Level 1 Academia

Location for 28th July 2023 is AC6-3 meeting room, level 6 Academia

Course Fees

	International Participants	Singapore Citizen 39 years old or younger	Singapore Citizen 40 years or older eligible for MCES	Singapore PRs	Enhanced 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$570.00	S$570.00	S$570.00	S$570.00
8% GST on Nett Programme Fee	S$152.00	S$45.60	S$45.60	S$45.60	S$45.60
Total Nett Programme Fee Payable, Incl. GST	S$2,052.00	S$615.60	S$615.60	S$615.60	S$615.60
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$2,052.00	S$615.60	S$235.60	S$615.60	S$235.60

Organisers

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

Trainers

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 27 June 2023. For enquries, kindly contact [email protected].

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.

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