Key Takeaways
By the end of this course, participants will be able to:
- Frame business problems as predictive analytics problems and select suitable regression or classification approaches.
- Build and evaluate predictive models using techniques such as linear regression, logistic regression, decision trees, ensemble methods, and introductory neural networks.
- Improve model performance through cross validation, hyperparameter tuning, and overfitting control.
- Interpret model results and translate predictive insights into practical business recommendations.
- Use Python or R, supported by AI tools, to develop more efficient and reliable predictive analytics workflows.
Who Should Attend
This course is for professionals who want to turn data into foresight, who want to move from analysing past performance to predicting future outcomes.
Whether you are predicting customer churn, forecasting demand, identifying operational risks, improving service delivery, or supporting smarter business decisions, this course will help you understand how predictive analytics and machine learning are applied in practice.
It is ideal for data analysts, business analysts, data scientists, aspiring machine learning engineers, IT professionals, and analytics practitioners who want hands on experience building predictive models. It is also useful for business, operations, product, and strategy professionals who need to work more effectively with data science teams and make better use of predictive insights.
Pre-requisites
- Participants should have prior experience in Python programming and foundational knowledge of statistics (e.g. regression, probability). Familiarity with basic linear algebra is beneficial.
- Participants should also be comfortable using AI-assisted tools to support coding and analysis.
What to Bring
No printed copies of course materials are issued.
Participants must bring their internet-enabled computing device (laptops, tablet etc) with power charger to access and download course materials.
If you are bringing a laptop, please see below for the tech specs:
| Minimum | Recommended |
Operating Systems | • Windows 7, 8, 10 or
• Mac OS | Laptop running the latest
version of either Windows or
Mac OS |
System Type | 32-bit | 64-bit |
Memory | 8 GB RAM | 16+ GB RAM |
Hard Drive | 256 GB disk size | |
Others | • An internet connection – broadband wired or wireless
• Installation permissions (non-company laptops)
• Keyboard
• Mouse/Trackpad
• Display
• Power adapter (laptop battery might run out) | DirectX 10 graphics card for graphics hardware acceleration
|
What Will Be Covered
- Introduction of real-world predictive problems and the fundamentals of regression and classification techniques - Participants will learn how linear regression models are developed, optimised, and applied to practical business problems through lectures, case studies, and hands-on workshops.
- Focus on variable selection methods and logistic regression for classification tasks - Participants will gain practical experience in selecting relevant variables, improving model performance through hyperparameter tuning and cross-validation, and applying logistic regression to real-world predictive scenarios.
- Decision tree models and their applications in predictive analytics - Participants will explore model optimisation, interpretability, and hyperparameter tuning while developing practical skills to build and evaluate decision tree models through workshops and case discussions.
- Introduction of ensemble methods and commonly used ensemble models for improving predictive performance - Participants will study optimisation and hyperparameter tuning techniques and apply ensemble models to solve real-world predictive problems in practical workshop sessions.
- Focus on neural network models and their architectures for predictive analytics - Participants will learn how to optimise and apply neural networks to real-world problems while gaining hands-on experience in building and evaluating neural network models
Fees & Subsidies
Fees for 2025
| |
Full Fee |
Singaporeans & PRs
(self-sponsored) |
| Full course fee |
S$4500 |
S$4500 |
| ISS Subsidy |
- |
(S$450) |
| Nett course fee |
S$4500 |
S$4050 |
| 9% GST on nett course fee |
S$405 |
S$364.50 |
| Total nett course fee payable, including GST |
S$4905 |
S$4414.50 |
Note:
- All fees and subsidies are valid from January 2024, unless otherwise advised.
- All self-sponsored Singaporeans aged 25 and above can use their SkillsFuture Credit to pay for course fees. For more information about SkillsFuture Credit, click here.
- From 1st January 2024, the GST will be increased to 9%.
Certificate
The ISS Certificate of Completion will be issued to participants who have attended at least 75% of the course and pass the required assessments.
Preparing for Your Course
NUS-ISS Course Registration Terms and Conditions
Find out more.
NUS-ISS and Learner’s Commitment and Responsibilities
Find out more.
WIFI Access
WIFI access will be made available to participants.
Venue
NUS-ISS
25 Heng Mui Keng Terrace
Singapore 119615
Click HERE for directions to NUS-ISS
In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.
Course Confirmation
All classes are subject to confirmation and NUS-ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by NUS-ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.
General Enquiry
Please feel free to write to ask-iss@nus.edu.sg if you have any enquiry or feedback.