Predictive Analytics and Machine Learning
- Analytics & Tech
- Artificial Intelligence
- Innovation & Business Improvement
This programme is conducted online.
Please refer to respective modules for dates.
Weeknights (7pm - 10:30pm)
Saturday (9am - 6pm)
Who Should Attend
- The course is intended to advance the knowledge and expertise of working professionals who already possess the fundamentals of R programming and data analytics, along with a basic understanding of statistical and causal inference modelling (modelling for explanation).
- The course is intended to advance such expertise, using base R and tidyverse ways of R programming for cutting-edge practices, to predictive models and machine learning.
- 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 is recommended (equivalent to that attained in Certified Data Analytics (R) Specialist programme)
Overview
The course is designed to equip participants with practical, tangible, and interpretable predictive analytics and machine learning using R language. The six modules provide step-by-step guidance to participants to train, and test supervised and unsupervised machine learning models in numerical, categorical, and text data.
Participants will get to discuss productivity tools and experience the deployment of machine learning models on various platforms like GitHub and its interactive dashboards. This practical six-module course will culminate in a capstone project, where they will work on their own predictive analytics project using machine learning.
Learning Objectives
- Equip with the fundamental concepts and systemic framework (workflow) of supervised machine learning.
- Learn the differences between solving regression and classification problems in the workflow of predictive models.
- Learn the essential concepts of dimensionality reduction using Tidymodels framework in the R data science ecosystem.
- Learn the core concepts of K-means and hierarchical clustering and differentiate from dimensionality reduction.
- Learn about supervised and unsupervised learning to text data and executing predictive models when given prediction tasks dealing with text.
- Learn how to store and deploy models and algorithms, using an interactive dashboard, web API, and Github.
Topic/Structure
To achieve the Certificate in Predictive Analytics and Machine Learning, participants will need to complete the following modules offered by SMU Academy in sequential order:
Predictive Modelling for Numerical Data
Predictive Modelling for Categorical Data
Dimensionality Reduction
K-Means and Hierarchical Clustering
Text Classification and Topic Modelling
Shiny and Machine Learning in Production
This course is also a part of the Advanced Diploma in Data and Predictive Analytics in R Programming.
Assessment
- Individual assessments
- Group assignments
CERTIFICATION
Upon completion of all 6 modules within a maximum duration of 3 years, participants will be awarded a digital Certificate in Predictive Analytics and Machine Learning.
Calculate Programme Fee
Fee Table
| COMPANY-SPONSORED | |||
|
PARTICIPANT PROFILE |
SELF-SPONSORED |
SME |
NON-SME |
|
Singapore Citizen < 40 years old Permanent Resident LTVP+
|
$3,139.20 (After SSG Funding 70%) |
$1,219.20 (After SSG Funding 70% |
$3,139.20 (After SSG Funding 70%) |
|
Singapore Citizen ≥ 40 years old |
$1,219.20 (After SSG Funding 70% |
$1,219.20 (After SSG Funding 70% |
$1,219.20 (After SSG Funding 70% |
|
International Participant |
$10,464 (No Funding) |
$10,464 (No Funding) |
$10,464 (No Funding) |
All prices include 9% GST
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
| Modules | Next Intake |
|---|---|
| Module 1: Predictive Modeling for Numerical Data | To be advised |
| Module 2: Predictive Modeling for Categorical Data | To be advised |
| Module 3: Dimensionality Reduction | To be advised |
| Module 4: K-Means and Hierarchical Clustering | To be advised |
| Module 5: Text Classification and Topic Modeling | To be advised |
| Module 6: Shiny and Machine Learning in Production | To be advised |
