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Foundation of Data Science

Part of the ModularMaster in Data Science programme

Empower your decisions with Data

Foundation of Data Science is a practical course which will provide participants with the fundamental knowledge, skills and abilities to extract, transform, analyse and model data.

The 5-day, credit-bearing programme provides an overview of data science and machine learning. Participants will learn through hands-on practices to prepare data for analysis, develop testing methods for statistical model, operationalise machine learning models and identify and communicate business insights to both technical and non-technical audience.

Plan your learning path

This course can be taken as a module on its own or as part of the Graduate Certificate in Fundamentals in Data Science stack and participants will earn 12 subject credits which can also be used towards completing the ModularMaster in Data Science.

Course Details

Course Dates
No course dates available currently.

Past Intakes
• Intake 7: 1 Jun - 27 Jul 2021
• Intake 7: 1 Feb - 22 Mar 2021
• Intake 6: 13 Oct - 1 Dec 2020


Who Should Attend

First step for participants who aspire to build their foundational knowledge and skills towards the role of a data or business analyst; or for participants who wish to be a “citizen data/business analyst” in their work and be able to solve day-to-day problems through data analytics and draw useful insights from their data.


Learner's Experience

"The course is well paced and the skills learnt are relevant to situations encountered in a professional setting. There are good opportunities for hands on practice and provides a good foundation and refresher to those in need of data science knowledge."

Joseph Huang
Attended Foundation in Data Science

Programme Outline

Learning Objectives
  1. Understand what is Big Data Analytics, how it is used and where it can be applied.
  2. Prepare, analyse, identify business insights, and apply data science and big data analytics.
  3. Develop, evaluate testing methods for statistical model.
  4. Operationalise the models further and more types of analysis model.
  5. Demonstrate through a presentation on how they have designed and applied a data analysis project  from start to finish and the key results of the analysis and insights gained.
Day 1 - The Data Science Ecosystem

Data Science Introduction
Data Science is one of the hottest topics across industries, understanding what data is and its applications is vital to businesses looking to understand, predict and evaluate business decisions. Be acquainted with the world of Data Science as you immerse yourself through the workflow of Data Science and its Pipeline, even by converting a simple office tool like Excel you can harness its capability and leverage on its potential to embark on your own Data Science Mini Expedition.

Stakeholder Analysis
Often the process of identifying the crucial people before embarking Data Science project is essential. These people may influence or are indirectly impacted by your project downstream. It requires grouping them according to the various paradigm of stakeholders based on various participation levels, interest and influence.

Data Discovery
The collection and analysis of data from various sources to gain insight from hidden pattern and trends, which is a crucial step for critical business decision.

Data Wrangling and Engineering
Data comes in all shapes and sizes, they can be unstructured or structured and come from all venues. Data Wrangling helps with "cleaning" or mapping of the raw data into more meaningful formats that can then be translated into more valuable data for the consumption of analytics purposes.

Data Science Modelling and Algorithm
Companies have benefited from leveraging upon the Data Science Models and Algorithms to make a critical business decision. This has upped the maturity of analytics capabilities to greater heights and has placed organisation at a greater advantage compared to the competitors, who are still in its infancy phase or the standard reporting stages. By positioning an organisation towards a more mature analytics phase have not only allowed the companies to reap immense growth and opportunities against its competitor, but also change the way we perceived the Data Science Modelling and Algorithm. This has led to various breakthrough and advancements away from the traditional algorithm approach such as the use of Deep Learning algorithm to fulfil a complex task and exemplifying the way we think about the traditional statistics.

Day 2 - Interactive Data Exploratory phase with preparation

Embarking on a Data Science Journey
The knowledge gained from Day 1, will allow participants to better appreciate the various means to kick off a Data Science project. Participants will be exposed to a methodical way of initiating a problem statement and have a better understanding of an industrial hands-on approach to various cases, such as the use of Coronavirus dataset. The understanding of adopting an empirical approach towards a data science project will better help practitioners leverage on a data-first mindset whenever it comes planning a critical project.

Data Exploratory Process
It entails the use of various interactive data exploratory approach as a means to characterise the existing dataset and better understand the dataset with Interactive Data Exploratory analysis with Excel. In this course, you will be exposed to the power of charts, descriptive statistics, data pivot and powerful yet simplistic functions.

Data Preparation
Data Preparation entails a process of cleaning, treating special types of data and handling its unstructured form into something that is more ingest-able by the algorithm, which is later used as a cleanly formatted dataset within the subsequent lifecycle of Data Science. Often it is infamously hailed as the most painful process by a few Data Scientists, who would eventually try to capitalise on all-in-one tool and package which are off-the-shelf than methodically stripping parts of the wrangling process manually. Indeed it is unavoidable no matter at which phase of an organisation's maturity in terms of analytics capability or at which stage of advancement within the Data Science Modelling and Algorithm.

Statistical Validation on Dataset
Validating dataset with statistics is often important yet technical enough to turn off some of the data analytics professional. However, the same cannot be said for those who find statistics useful in particular to validate the normality of the dataset or to handle the biasedness is the process of sampling a dataset. This can help experts avoid the pitfalls in the later phase of the data science lifecycle, which as the old adage says "garbage in garbage out".

Day 3 - Regression Modelling

Regression Modelling
Regression Modelling has been coined as the most common algorithm of all model paradigms. It has been often used in various paradigms from statistical analysis to machine learning and even advanced analytics or as a backtester option for portfolio analysis. It comes in all shapes and sizes from multiple linear regression to its family of regression such as logistic regression or lasso. In this short course, we will explore the intuition of regression and the role it plays from use cases such as predicting a loan default to possibility of contracting a disease or virus.

Day 4 - Data Storytelling with Visualisation

Data Visualisation and Storytelling
Being able to communicate data to different stakeholders is critical, coupled with time-poor executives and shortened attention-span of the audience, data visualisation if done right can help the audience understand the data better and quicker.
Data storytelling takes data visualisations and turn them into narratives which helps the audience to quickly understand the insights and increase the impact of your data.

Day 5 - Project Presentation

Project Presentation
Participants will demonstrate through a presentation on how they have designed and applied a data analysis project from start to finish and the key results of the analysis and insights.

Course Fees and Funding

SkillsFuture Course Fee Subsidy

Fee after subsidy

GST on Fee after SSG Course Fee Subsidy

You pay

Mid-career Enhanced Subsidy (MCES)

Fee after subsidy

GST on Fee after SSG Course Fee Subsidy

You pay

Enhanced Training Support for SMEs (ETSS)

Fee after subsidy

GST on Fee after SSG Course Fee Subsidy

You pay

Full Course Fee (without subsidies): $4,815 (inclusive of prevailing GST)


Johnson Ang
Instructor, SUTD Academy

Johnson Ang has a Masters in IT Analytics from SMU and is certified and a member of CQF, ACTA and CEH. He previously work as a Data Scientist with MND and was an ex-affiliate University lecturer for Cardiff Metropolitan UK University and ex-lecturer with EASB in 2018 and was also Corporate and Open-Run trainer for 2 years.

His experience spans more than 10 years of working experience, of which 4 years of experience in the Banking Wide Operations and has been particularly involved in IT Analytics banking projects within the Risk Management and Financial Instrument sphere, and 4 years in the Real Estate, FMCG, Construction and Education Sector. He is familiar with typical banking software and acquainted with popular tools such as SAS, JMP, Oracle, VBA, Access, Python, R, Tableau, Weka, C and Java. He also has working knowledge in the area of Data Science; Analytics, Machine Learning and Deep Learning.

ModularMaster Certificate in Data Science
Course Structure and Pathways

Two Tracks
We have designed two tracks to cater to participants with different learning preferences.

​Participants who prefer to make use of programming can go for the modules under the Programming Track while participants who prefer to make use of readily available tools can embark on modules under the Non-Programming Track.

For participants who do not possess a programming background, you may also take on a Bridging pathway towards the Programming Track.


Policies and Financing Options

SSG Funding Terms and Conditions

Use of Personal Details

In consideration of the subsidy provided by SkillsFuture Singapore Agency (“SSG”) through the SUTD Academy for the Course,

I consent to:

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:

  1. Reporting of national statistics and conducting of holistic continuing education training research and analysis;

  2. Facilitate the conduct of the relevant surveys and audits in relation to the Course;

  3. General administration of the Course including but not limited to processing of the subsidy provided by SSG;

  4. Publicity and marketing of the Course or other Courses to be provided by SSG or SUTD Academy; and

  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:

  1. Attend and complete all lectures, class exercises, workshops and assessments;

  2. Complete the Course feedback at the end of the Course;

  3. Complete the post Course survey sent about 3 to 6 months after class attendance; and

  4. Sign up for a personal email account.

SUTD Privacy Statement

For more information on SUTD's privacy statement, please visit

SUTD Terms and Conditions

Methods of Payment

Learn more about the available payment modes.

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.

Replacement Policy

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.

  2. Course enrolment is based on a first-come, first-served basis.

  3. SUTD Academy reserves the right to change or cancel any course or instructor due to unforeseen circumstances. 

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).

    For more info, please visit  SkillsFuture website at

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
    - At least 30% local shareholding by Singapore Citizens or Singapore Permanent Residents
    - Employment size of not more than 200 (at group level) or with annual sales turnover (at group level) 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

    For more info, please visit SSG website at

Funding under Union Training Assistance Programme ("UTAP")

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).

For more info, please visit