Data Science Modelling with Programming

Part of the ModularMaster in Data Science (Healthcare) programme

Data Science Modeling is a fundamental component of the data science workflow, encompassing a wide range of techniques and algorithms used to extract insights and make predictions from data. It involves the understanding and application of mathematical and statistical models to uncover patterns, relationships, and trends in complex datasets. Data professionals utilize various modeling approaches, such as supervised learning and unsupervised learning, depending on the nature of the problem and the available data. Through model selection, training, and evaluation, they aim to create accurate and robust models that generalize well to unseen data. Data science modeling plays a crucial role in diverse applications, including but not limited to predictive analytics, recommendation systems, fraud detection, and image recognition. By leveraging the power of modeling techniques, data professionals can harness the value of data and derive actionable insights, ultimately driving informed decision-making and generating positive impact across industries.

This course, spanning a duration of five days, is specifically designed to equips participants with skills on basic supervised and unsupervised machine learning models in healthcare settings. Participants will learn to leverage these models appropriately and evaluate various parameters based on the consequences of the predicted model. The course aims to equip individuals with the necessary skills to apply machine learning techniques in healthcare scenarios. Over the first four days, participants will gain insights into the selection and implementation of machine learning models, considering their potential impacts in healthcare decision-making. Participants will be actively involved in a healthcare-related project throughout the module. The final day, which is split into two half-days on separate weeks, will be dedicated to project consultation and project presentation.

Plan your learning path

This course can be taken as a module on its own or as part of the Graduate Certificate in Data Analytics (Healthcare) or ModularMaster in Data Science (Healthcare).

Course Details

Course Dates:
No available course dates


Who Should Attend

Catering to healthcare professionals and individuals aspiring to join the healthcare industry, this course is specifically designed to develop essential skills in data science models and algorithms encompassing regression, classification, clustering, and dimensionality reduction. It is highly recommended for clinicians, administrators, and managers who aim to comprehend the strengths and limitations of different models based on dataset characteristics, as well as preparing aspiring data analysts or data scientists to apply these models effectively in addressing healthcare data challenges.


  • Participants should preferably have passed mathematics at least ‘O’ Level or equivalent.

  • Participants should preferably have basic knowledge of statistic.

  • Participants should be conversant with basic IT skills such as software installation, file management and web navigation.

  • Participants are encouraged to complete the Data Wrangling and Preparation with Programming and Data Validation and Statistical Analysis with Programming before enrolling in this course.

  • Participants are required to pass a pre-course assessment to ensure participants have the requisite knowledge of Python programming. This assessment can be waived if participants have completed both Fundamentals in Python (Basic) and Fundamentals in Python (Intermediate).

  • Participants are required to bring their laptops.

Programme Outline

Learning Objectives and Structure
  • Gain a good understanding of the underlying principles and concepts of fundamental data science models and algorithms, including regression, classification, clustering, and dimensionality reduction.
  • Demonstrate proficiency in assessing the strengths and limitations of various data science models and algorithms given the characteristics of the dataset.
  • Apply practical skills to effectively implement the appropriate data science models and algorithms to resolve business problems.
  • Identify and interpret patterns, trends, and meaningful insights within datasets using the applied data science models and algorithms.
  • Acquire the ability to analyze and compare the results obtained from different data science models and algorithms to extract and enhance insights and predictions.
  • Understand healthcare case studies shared by SingHealth faculty members to gain insights into real-world scenarios.
  • Utilise curated public healthcare datasets to perform hands-on activities and assignments, fostering practical experience and understanding of the subject matter.
Day 1
  • Basics of Statistics
  • Application of Statistics in Real World
  • Introduction to Quantitative Intuition for Statistics
  • Steps in Hypothesis Testing
  • Z Test
  • T Test
Day 2
  • Overview of Data Science
  • Data Science Pipeline
  • What is Machine Learning Model?
  • Understand what is required prior to using machine learning model
  • Understand how data scientist trains machine learning model
  • Data Preparation and Data Validation
  • Train-Test Split and Cross Validation
  • An introduction to Supervised and Unsupervised Learning
Day 3
  • Data Preparation for Linear Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Evaluating Linear Regression Models Performance
  • An extension of regression on Correlation, Covariance and Multi-collinearity Issues
  • Remedies for Multi-Collinearity
  • Data Preparation for Logistic Regression
  • Logistic Regression Model
  • Evaluating Logistic Regression Models Performance
Day 4
  • Data Preparation for Clustering
  • K-Means Clustering
  • Dimensionality Reduction
  • Basics of Principal Component Analysis
  • Interpreting Principal Component Analysis Results
Day 5 - Consultation / Project presentation

Project Consultation

Each group of participants will present the progress of their projects and have the opportunity to ask questions and clarify any doubts pertaining to their projects.

Project Presentation

Each group of participants will showcase their work and respond to questions during a Q&A session.

Course Fees and Funding

Full course fee inclusive of prevailing GST

You pay

SkillsFuture Course Fee subsidy (70%)

  • For Singapore Citizens < 40 years old 
  • For Permanent Residents

You pay

Mid-Career Enhanced Subsidy (90%)

  • For Singapore Citizens ≥ 40 years old

You pay

Enhanced Training Support for SMEs (90%)

  • For SME - Sponsored employees

You pay

The above module fee payable is inclusive of 9% GST. 

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Thia Wei Soon
Instructor, SUTD Academy

Wei Soon has more than ten years of experience working in the manufacturing and IT sectors. He worked as a data scientist using data analytics and machine learning to deliver actionable insights and drive strategic marketing initiatives. In recent years, as a technology consultant, he successfully helped clients to streamline enterprise operations and achieved cost saving through the adoption of robotic process automation.

Wei Soon has a Master of IT in Business Artificial Intelligence from Singapore Management of University and a B.Eng in Mechanical Engineering from Nanyang Technological University. He is proficient with tools such as Tableau, Jupyter, RStudio, MS Visual Studio, Automation Anywhere, UiPath, and programming languages such as Python, R, C#, HTML5, and JavaScript.


Narayan Venkataraman
Assistant Director, Data Management & Informatics, Changi General Hospital

Narayan (Nari) is a Data Science and Biomedical professional with more than 22 years of experience in healthcare with diverse portfolio spanning data science, health informatics, data governance, medical technology, clinical quality and operational analytics, patient safety and risk management.
He is currently the Assistant Director, Data Management & Informatics at Changi General Hospital, Singapore. Recipient of the Singapore Commendation Medal 2022 for Covid19, he is a member of the CGH Covid19 Taskforce and many strategic committees at CGH and SingHealth (SHS). He has completed many medical projects across the Asia-Pacific region representing Singapore MOH and MFA. He is also an honorary biomed consultant for Smiles Asia and has volunteered for many surgical missions in Asia and Oceania. His current academic interests cover robotic process automation, AI/Machine Learning, data visualisation, risk analytics and enterprise data literacy.


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Funding under Mid-Career Enhanced Subsidy ("MCES")

  1. MCES is an enhanced Subsidy to encourage mid-career individuals to upskill and reskill, thereby helping them to remain competitive and resilient in the job market. With this, all Singaporeans aged 40 and above will receive higher subsidies of up to 90% course fee subsidy for SSG-funded certifiable courses.

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  2. SMEs will enjoy subsidies of up to 90% of the course fees when they sponsor their employees for SSG-funded certifiable courses.

  3. In addition to higher course fee funding, SMEs can also claim absentee payroll funding of 80% of basic hourly salary at a higher cap of $7.50 per hour. SMEs may apply for the absentee payroll via the SkillsConnect system.

  4. To qualify, SMEs must meet all of the following criteria:
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NTUC members enjoy 50% unfunded course fee support for up to $250 each year when you sign up for courses supported under UTAP (Union Training Assistance Programme).

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