Data Validation and Statistical Analysis with Programming

Part of the ModularMaster in Data Science (Healthcare) programme

Data Validation and Statistical Analysis are critical components in the field of data science, playing a vital role in ensuring the accuracy, reliability, and meaningful interpretation of data. Through data validation techniques, professionals can verify the integrity and quality of the collected data, identifying and addressing any inconsistencies, errors, or outliers that may impact the analysis. By conducting rigorous statistical analysis, they gain insights into patterns, relationships, and trends within the data, enabling evidence-based decision-making. Statistical analysis allows for hypothesis testing, correlation analysis, regression modeling, and other techniques to draw valid conclusions and make reliable predictions. The combination of data validation and statistical analysis ensures that data-driven insights are robust, trustworthy, and actionable, empowering organizations to make informed choices and achieve meaningful outcomes.

This course, spanning a duration of five days, is specifically designed to equips participants with valuable skills in data exploration, statistical anomaly detection, and dataset validation in healthcare contexts. Over the first four days, participants will learn essential skills related to exploring data distribution, detecting statistical anomalies, and statistically validating datasets. The course will equip healthcare professionals with the necessary tools to analyse healthcare data effectively and make informed decisions based on statistical evidence. 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 2023:
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 validation and statistical analysis of data. It is highly recommended for clinicians, administrators, and managers who involve in crucial task of finding patterns in data and making inferences about those patterns, as well as preparing aspiring data analysts or data scientists to perform rigorous statistical testing on healthcare data.


  • 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 Foundation of Data Science and Data Wrangling and Preparation 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
  • Perform the statistical validation component for the role of a junior data scientist or statistical researcher
  • Validate dataset statistically to support data analysis and modelling
  • Explore on data distribution and identify statistical anomalies
  • Compute Statistical Indicators for the dataset
  • Check normality assumption for a data series
  • Perform statistical test and validation on dataset
  • Conduct further statistical analysis
  • 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
  • Overview of Data Science Pipeline
  • What is Data Validation and Statistical Analysis?
  • Importance of Statistics in Data Validation for Machine Learning
  • Requirements prior to Data Validation phase
  • Understand how data scientist leverage on Data Validation and Statistics
  • Basics of Statistics and Hypothesis testing
Day 2
  • Statistical Test on Dataset Characteristics
  • Probability and Expectations
  • Central Tendencies and Dispersion
  • Central Limit Theorem
Day 3
  • Understanding Dataset characteristics or differences
  • Parametric Test
  • Introduction to Interval and Ratio Data
  • Central Limit Theorem
  • z - test
  • t - test
  • Parametric ANOVA (Interval Data or F-Test)
  • Understanding Dataset variables relationship
  • Spearman r
  • Pearson r
Day 4
  • Understanding Dataset characteristics or differences
  • Non-Parametric Tests
  • Introduction to Ordinal Data 
  • Non-Parametric ANOVA (Ranked Data - Friedman Test)
  • Introduction to Categorical Data Analysis
  • Goodness of Fit - Chi Square (Categorical Data)
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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In consideration of the subsidy provided by SkillsFuture Singapore Agency (“SSG”) through the SUTD Academy for the Course,

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The collection, use and disclosure to relevant third parties of my personal data by the SUTD Academy including but not limited to personal particulars, attendance records, assessment/performance records, for the following purposes:

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  5. SSG or its Appointed Auditors or Nominated Representatives to directly contact Course Participant to obtain information deemed necessary for the purposes of conducting effectiveness survey or audits in relation to the Course.

I agree to:

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Cancellation & Refund Policy

  1. If a written notification is sent to within 24 hours after course registration deadline there will be no cancellation charges. A full refund will be made. 

  2. No refund is provided if written notification is more than 24 hours after course registration deadline. SUTD Academy reserves the rights to collect the full fee amount from the participant.

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Companies may replace participants who have signed up for the course by giving a 3-working day notice before the course commencement date to Terms and conditions apply.

Registration Policy

  1. Course may be cancelled due to insufficient participants. SUTD Academy will not be responsible or liable in any way for any claims, damages, losses, expenses, costs or liabilities whatsoever (including, without limitation, any direct or indirect damages for loss of profits, business interruption or loss of information) resulting or arising directly or indirectly from any course cancellation.

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Types of Funding

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.

  2. Individuals/employers are not required to submit an application for the MCES. Those pursuing SSG-funded programmes will be charged the appropriate subsidised fees by SUTD Academy if they are eligible MCES. Individuals/employers will only need to pay the nett fee (full course fee after SSG's grant).

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Funding under Enhanced Training Support for SMEs ("ETSS")

  1. ETSS is an enhanced funding to enable SMEs to send their employees for training.

  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:
    - Organisation must be registered or incorporated in Singapore
    - Employment size of not more than 200 or with annual sales turnover of not more than $100 million
    - Trainees must be hired in accordance with the Employment Act and fully sponsored by their employers for the course
    - Trainees must be Singapore Citizens or Singapore Permanent Residents

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UTAP is a training benefit for NTUC members to defray their cost of training. This benefit is to encourage more union members to go for skills upgrading.

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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