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Instructor-led Classroom Adult Training in Singapore - Modular Fast Track Skill-Based Trainings

Advanced Data Analytics and Machine Learning with R (CITREP+)

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention

Many industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data, organizations are able to work more efficiently or gain an advantage over competitors.

This course will start with the R primer and fundamental on Day 1 and introduce R Machine Learning on Day 2 and 3. Tthis 3 days course on Machine Learning will teach you most of the key machine learning using R. R is particularly suited to learning machine learning due to user friendliness and the powerful RStudio IDE

Course Highlights

  • R Fundamental 
  • Overview of Machine Learning
  • Supervised Learning Models
  • Unsupervised Learning Models
  • Neural Network

Learning Outcomes

By the end of the training, learners will be able to:

  • R fundamental
  • Identify appropriate machine learning methods to address the problems and issues
  • Perform data analysis using machine learning methods
  • Draw inferences from the data analysis
  • Using R for machine learning

Target Audience

  • NSF
  • Full Time Students
  • Data Analysts

IMDA CITREP+ 70%-100% Funding

IMDA CITREP+ funding is only applicable to Singaporeans and PR. Subject to eligibility, the funding support is from 70% -100% . For more information on IMDA CITREP+ funding, click the link below

IMDA CITREP+ Funding Support

Please fill up the IMDA CITREP+ eligibility form after you have registered for this course

CITREP+ Trainee Eligibility Form

For Full Time Students, please sign off the PSEI form before the class start



All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

Course Code: CIT005862

Course Booking

$900.00 (GST-exclusive)

Course Date

Course Time

* Required Fields

Course Cancellation/Reschedule Policy

We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
Note the venue of the training is subject to changes due to class size and availability of the classroom.
Note the minimal class size to start a class is 3 Pax.

Course Details

Day 1

Topic 1. Getting Started in R

  • What is R
  • Install R and RStudio IDE
  • Explore RStudio Interface
  • R Script
  • Comment

Topic 2. Data Types

  • Numbers
  • String
  • Vector
  • Matrix
  • Array
  • Data Frame
  • List
  • Factor

Topic 3. R Packages & Datasets

  • Import R Packages
  • Import R Data Sets
  • Import External Data
  • Export Data

Topic 4. Data Visualization

  • Scatter Plot
  • Boxplot
  • Bar chart
  • Pie chart
  • Histogram

Topic 5. R Programming

  • Control Structures
  • Loop
  • Break & Next
  • Function

Topic 6. Statistics Analysis with R

  • Descriptive Statistics
  • Correlation
  • Linear and Multiple Regression
  • Hypothesis Testing
  • Analysis of Variance (ANOVA)

Day 2

Topic 7 Introduction to Machine Learning

  • What is Machine Learning
  • Types of Machine Learning
  • Supervised vs Unsupervised Learning
  • Python vs R for Machine Learning
  • Install R Machine Learning Package

Topic 8 Data Preprocessing

  • Sample Data
  • Impute Missing Data
  • Normalize Data
  • Split Data

Topic 9 Regression Methods

  • What is Linear Regression
  • Regularization - Bias vs Variance Tradeoff
  • Lasso Regression
  • Ridge Regression

Topic 10 Classification Methods

  • What is Classification
  • Logistic Regression
  • Gaussian Naive Bayes (GNB)
  • K Nearest Neighbor (KNN)
  • Support Vector Machine (SVM)
  • Decision Tree
  • Confusion Matrix
  • ROC and AUC Analysis


  • Written Assessment (Q&A)

Day 3

Topic 11 Clustering Methods

  • Distance Measure
  • K Means Clustering
  • Hierarchical Clustering
  • Silhouette Analysis

Topic 12 Ensemble Methods

  • Types of Ensemble Methods
  • Random Forest Ensemble
  • Gradient Boost and XGBoost Ensemble
  • Stacking Ensemble

Topic 13: Hyperparameter Tuning

  • Exhaustive Grid Search
  • Random Search

Topic 14: Neural Network

  • What is Neural Network
  • Activation Functions
  • Deep Learning vs Machine Learning
  • Classification Using Neural Network


  • Capstone Project

Course Admin


The learner must meet the minimum requirement below :

  • Read, write, speak and understand English

Target Audience

  • NSF
  • Full Time Students
  • Data Analysts

Software Requirement

Please download and install the following software prior to the class

Funding Validity Period

Valid from 21/06/2019 to 31/03/2021

Mode of Training

Instructor-led Classroom Training

CITREP+ Claim Procedure

Trainees who wish to claim for CITREP+ funding must submit their online claim applications to IMDA via ICMS upon course or certification completion. Please refer to the Claim Application Guide for detailed application procedures.

For Organisation-Sponsored Trainees, the claim application will be submitted by the sponsoring organisation.

For Self-Sponsored Trainees, the claim application has to be completed by the individual.

All claims for CITREP+ disbursement must be submitted to IMDA within three (3) months from completion date of the last examination or final post-training assessment. Late submissions will not be accepted. Applications with incomplete supporting documents will be rejected for processing.

CITREP+ Funding Support

Category Type Training course and certification
Organisation- sponsored Non SMEs Up to 70% of the nett payable course and certification fees, capped at $3,000 per trainee
SMEs Up to 90% of the nett payable course and certification fees, capped at $3,000 per trainee
Professionals (40 years old and above)
Self-Sponsored Professionals Up to 70% of the nett payable course and certification fees, capped at $3,000 per trainee
Professionals (40 years old and above) Up to 90% of the nett payable course and certification fees, capped at $3,000 per trainee
Students and/or Full-Time National Service (NSF) Up to 100% of the nett payable course and certification fees, capped at $2,500 per trainee


Who Should Attend

  • NSF or Full Time Students
  • Data Analysts
  • Marketeers


R Machine Learning TrainerDr. Ravi Kumar Tiwari got his PhD from NUS (Chemical Engineering) in 2013. After graduation, he worked 3 years as a research scientist in the Institute of High Performance Computing (IHPC). He is currently a big data R data analyst in Rakuten. His core skills are R, big data, Hadoop and machine learning.

R Machine Learning TrainerDwight Nuwan Fonseka have a degree in Biotechnology (from NUS) ,Advanced diploma in Pharamceutical management (from MDIS) and Masters in Education (from NTU). He have 8 years experience of teaching biology at O and A levels/ IB level in international schools in Singapore and overseas.


R Machine Learning TrainerHerman Tan is a certified Business Intelligence Professional by TDWI (The Data Warehouse Institute). He has developed analytics solution for businesses for the last 15 years. He is proficient in SQL, data management using Pentaho and Microsoft SQL Server, Oracle, and predictive analytics using open source tools mainly in R. Herman has conducted hands-on courses in R and Pentaho for Banking and Retail as well as instructor for NUS Masters of Science in Business Analytics program.

R Machine Learning TrainerDr. Zhu Tianming did her PhD in NUS, major in Statistics, and will graduate in August 2017. She has been a part-time teaching assistant for more than three years in NUS. She has taught the modules related to probability, regression analysis, categorical data analysis and multivariate statistical analysis. Her research interests are functional data classification and her core skills are R, machine learning and statistical analysis.

R Machine Learning TrainerDr Brandon Ooi has a Bachelors degree in Computer Engineering and a PhD in Bioengineering from the National University of Singapore. He has published papers on machine learning, bioinformatics and microarray data analysis. He has six years of teaching experience at the polytechnic level and was also involved in the creation and teaching of modules for adult learners.

R Machine Learmning TrainerDr Zheng Zejun has seven years’ experience in the data-mining field. Zejun worked as a (Sr.) bioinformatics scientist for six years and currently works as a data scientist manager. Dr. Zheng;s expertise covers machine learning, statistics, algorithm design, bioinformatics and high performance computing. He has published three machine learning algorithms on well-recognized academic journals together with the open source software (CUDA-CRISY, FSOM, DYSC). Zejun is professional with an extensive set of programming languages, including C/C++, Python, R, JAVA and flask for data driven analytics and scalable computing. He has also a broad knowledge of algorithms and mathematical models in the data-mining field.

R Machine Learning TrainerWesley Goi is currently in his final year of his PhD in Bioinformatics at National University of Singapore (NUS) where he previously received his degree in Molecular Biology at NUS (Honours 2nd Uppers). He was the TA for Introductory Bioinformatics LSM2241. He specialises in analysing high throughput DNA and RNA sequencing data of complex microbial communities using network analyses and various functional analyses. In his previous projects he has applied machine learning methods to vaccine discovery.

Data Science TrainerDr. Shailey Chawla is an experienced academician and researcher with over 10 years of experience in teaching and research. Her PhD research topic was on Web Requirements Engineering. She has published various research papers and book chapters in reputed international journals and conferences. Her recent research has been in Big Data Analytics at Hong Kong Polytechnic University where she worked on Urban Data Analytics and Educational Data Mining. She is proficient in R, Python, Java, Advanced Excel, Data Mining, C, Linux programming among various other core computing subjects.

R Machine Learning TrainerAriff is a recent Graduate from Murdoch University in BSc in Business Information System and Management (Double Major). He is a MOE Registered Instructor teaching in most of the Information Technology and Multimedia aspects such as Web programmer, Mobile App Developer, Video director and editor, and game development and design (PC and Mobile). Besides Information Technology and Multimedia, he is also giving entrepreneurship, leadership workshops and motivation speech training.

He has a strong passion in IT and gaming design been since 2005. He is currently doing games design for his own project and continues to teach and share his knowledge. He still hungers for new skills to keep himself up to date as technology continues to change.

R Machine Learning TrainerSiva Kumar is an experienced Solutions Architect with a demonstrated history of working in the computer software industry. Skilled in J2EE Web Services, Oracle Database, Maven, C++, and Apache Kafka. He is a strong engineering professional with a Bachelor of Technology (B.Tech.) focused in Computer Science from JNTU University Hyderabad.

Customer Reviews (3)

will recommend Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
. (Posted on 5/31/2020)
Marcel is a very dedicated and patient tutor Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Have a day gap in between for productivity, 3 days in a row is too much.

Marcel is a very dedicated and patient tutor, 100% recommend him to anyone. Very very thankful. :) (Posted on 4/10/2020)
Will Recommend Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
. (Posted on 9/12/2019)

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