Module 1 - Machine Learning Data Pipelines and Visualisation Mastery with Python
- Analytics & Tech
- Artificial Intelligence
- Innovation & Business Improvement
This programme is conducted on-campus.
To use your SkillsFuture Credit, please submit your claim through our payment portal. Do not submit the claim manually via the SkillsFuture page. Please refer to our step-by-step guide here.
3 days
Weeknights (6.30pm - 10:45pm)
Saturday (9am – 6.15pm)
Singapore Management University
Who Should Attend
- Professionals and managers in data science, engineering, or IT seeking to upskill in machine learning and AI
- Individuals transitioning into machine learning roles who have basic Python or programming experience
- Industry practitioners looking to understand and implement AI-driven solutions for business challenges
- Analysts or researchers aiming to apply ML/ AI techniques to data-driven decision-making processes
PREREQUISITE
- Basic programming knowledge is recommended to enrol for this programme
SYSTEM REQUIREMENTS
- Functional Laptop: (1) CPU must be of at least intel core I3, (2) GPU must have an integrated graphics card and (3) RAM must be of at least 4GB
Overview
Participants will learn how to streamline data extraction, transformation, and loading (ETL) processes while ensuring data quality for machine learning workflows. By mastering Python libraries and tools, they will create interactive dashboards and visualisations that enable actionable insights. This module forms the backbone of an efficient machine learning pipeline, preparing participants to handle real-world data challenges confidently.
Note: This module is part of a sequential programme and is not available on a standalone basis.
Learning Objectives
- Design and implement scalable data pipelines for machine learning workflows
- Perform Extract, Transform, Load (ETL) processes using Python libraries
- Apply advanced data preprocessing and feature engineering techniques
- Create dynamic, interactive dashboards using Python
- Optimise workflows for efficiency and reproducibility in machine learning tasks
Topic/Structure
- Introduction to Data Pipelines and Clean Architecture
- Setting up Python environments for ML and ETL fundamentals
- Data Preprocessing and Feature Engineering and Managing Data Quality and Handling Missing Data
- Visualisation Principles and Creating Dynamic Dashboards with Python
- End-to-End Pipeline Construction and Integrating Data Pipelines with Dashboards for ML Workflows
Assessment
- Classroom exercises
- Group assignments
- Individual assessment
CERTIFICATION
Upon meeting the attendance and assessment criteria, participants will be awarded a digital certificate for participating in each module. Please refer to our course policies to view the attendance and assessment criteria.
Upon completion of all modules required for this programme within a maximum duration of 3 years, participants will be awarded a digital certificate.
Calculate Programme Fee
Fee Table
| EMPLOYER-SPONSORED | |||
|
PARTICIPANT PROFILE |
SELF-SPONSORED |
SME |
NON-SME |
|
Singapore Citizen < 40 years old Permanent Resident LTVP+
|
$784.80 (After SSG Funding 70%) |
$304.80 (After SSG Funding 70% |
$784.80 (After SSG Funding 70%) |
|
Singapore Citizen ≥ 40 years old |
$304.80 (After SSG Funding 70% |
$304.80 (After SSG Funding 70% |
$304.80 (After SSG Funding 70% |
|
International Participant |
$2,616 (No Funding) |
$2,616 (No Funding) |
$2,616 (No Funding) |
All prices include 9% GST
Please note that the programme fees are subject to change without prior notice.
Post Secondary Education Account (PSEA)
PSEA can be utilised for subsidised programmes eligible for SkillsFuture Credit support. Click here to find out more.
Self Sponsored
SkillsFuture Credit
Singapore Citizens aged 25 and above may use their SkillsFuture Credits to pay for the course fees. The credits may be used on top of existing course fee funding.
This is only applicable to self-sponsored participants. Application to utilise SkillsFuture Credits can be submitted when making payment for the course via the SMU Academy TMS Portal, and can only be made within 60 days of course start date.
Please click here for more information on the SkillsFuture Credit. For help in submitting an SFC claim, you may wish to refer to our step-by-step guide on claiming SkillsFuture Credits (Individual).Workfare Skills Support Scheme
From 1 July 2023, the Workfare Skills Support (WSS) scheme has been enhanced. Please click here for more details.
Employer Sponsored
Enhanced Training Support for SMEs (ETSS)
- 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
- Trainees must not be a full-time national serviceman
- Trainees are eligible for ETSS funding only if their company's SME status is approved prior to the course commencement date. To verify your SME's status, please click here.
Please click here for more information on ETSS.
Absentee Payroll
Employers who sponsor their employees for the course may apply for Absentee Payroll here. For more information, please refer to:
AP Guide (Non-SME Companies)
Declaration Guide (SME Companies)
Intake Information
Next Intake: 13, 14 & 17 Jan 2026 [Open for Registration]
