Predictive Analytics and Machine Learning Module 4: K-Means and Hierarchical Clustering (Synchronous e-Learning)
- Analytics & Tech
- Artificial Intelligence
- Innovation & Business Improvement
This programme is conducted online.
3 days
Weeknights (7pm - 10:30pm)
Saturday (9am - 6:00pm)
Who Should Attend
- Participants who wish to advance their expertise in predictive models and machine learning, along with productivity tools for data science (e.g., creating and maintaining github and interactive dashboards), beyond the cornerstone knowledge and expertise on data analytics and visualisation, will find it a great fit and will learn the most on this course.
- Targeted Job Roles: Data analyst, Statisticians, Data scientist, Data Architect, Quantitative Analysis with R, System Intelligence Manager, Trading analyst
PREREQUISITES
Completion in R programming (equivalent to that attained in Certified Data Analytics (R) Specialist programme)
Overview
Building on the previous module, participants will further develop their expertise in unsupervised learning. They will learn the core concepts of K-means and hierarchical clustering, and how they are related to but differ from dimensionality reduction and when they can aid supervised learning.
They will also learn how to execute clustering by applying Tidy data principles and solving real-world business problems like market segmentation, customer journey mapping and investor clustering.
This module is part of a sequential programme and is not available on a standalone basis.
Learning Objectives
At the end of the 2-day module, participants will be able to:
- Understand the essential concepts of K-means and hierarchical clustering.
- Understand what types of problems can be solved by K-means and hierarchical clustering.
- Execute K-means and hierarchical clustering using Tidy data principles.
- Know the similarities and differences between dimensionality reduction and clustering, and their relation to supervised learning.
Topic/Structure
- Understanding K-means Clustering
- Running K-means Clustering
- Exercises on K-means Clustering for a Problem-Solving in Data Analytics
- Understanding Hierarchical Clustering
- Running Hierarchical Clustering
- Exercises on HierarchicalClustering for a Problem-Solving in Data Analytics
- Business applications of clustering on market segmentation (and customer journey mapping)
- Business applications of clustering on investor clustering (and stock clustering)
- Group Assignment
- Capstone Project Briefing
Assessment
- Group Assignment
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
| COMPANY-SPONSORED | |||
|
PARTICIPANT PROFILE |
SELF-SPONSORED |
SME |
NON-SME |
|
Singapore Citizen < 40 years old Permanent Resident LTVP+
|
$523.20 (After SSG Funding 70%) |
$203.20 (After SSG Funding 70% |
$523.20 (After SSG Funding 70%) |
|
Singapore Citizen ≥ 40 years old |
$203.20 (After SSG Funding 70% |
$203.20 (After SSG Funding 70% |
$203.20 (After SSG Funding 70% |
|
International Participant |
$1,744 (No Funding) |
$1,744 (No Funding) |
$1,744 (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.
Company 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 will be able to enjoy ETSS funding only if the company's SME's status has been approved. To verify your SME's status, please click here.
Please click here for more information on ETSS.
Absentee Payroll
Companies 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: To be advised
