CET803 DS2: Predictive Analytics

Course Provider

School of Computer Science and Engineering (SCSE)



Academic Unit



This course aims to teach the basics of classical machine learning through hands on experiments in prediction and classification. This is the main component in a modern data science pipeline

This course is part of:

- Graduate Certificate in Data Science and Artificial Intelligence
- Graduate Certificate in Artificial Intelligence and AI Plus 
FlexiMasters in Data Science and Artificial Intelligence
- FlexiMasters in Artificial Intelligence and AI Plus

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At the end of the course, learners  are able to:

1. build and train models on labelled datasets;

2. check model accuracy, and finally;

3. predict values or classes of target variables using those models.

1. Introduction to basics of Machine Learning
2. Linear Models for Predicting Numeric Variables
3. Tree Models for Predicting Categorical Variables
4. Concept of Bias and Variance in Model Fitting.

Python codes relevant to this module will be provided and will be used for hands-on demonstrations during the interaction session.

For learner who wish to acquire the knowledge and skills to boost their career prospects, become savvier in technology applications as well as better equipped for the fast paced advancements expected ahead.

Standard Course Fee: S$1,944

SSG Funding Support

Course fee payable after SSG funding, if eligible under various schemes

Fee BEFORE funding & GST

Fee AFTER funding & 8% GST

Singapore Citizens (SCs) and Permanent Residents (PRs) (Up to 70% funding)



Enhanced Training Support for SMEs (ETSS)


SCs aged ≥ 40 years old
SkillsFuture Mid-career Enhanced Subsidy (MCES)
(Up to 90% funding)

• NTU/NIE alumni may utilise their $1,600 Alumni Course Credits. Click here for more information.

    Read more about funding
    CET787 Foundations of Computation Thinking and Programming 1
    CET788 AI 1:AI Foundation1
    CET789 AI 2: Reinforcement Learning1
    CET790 AI 3: Computational Game Theory1
    CET791 ML1: Supervised learning: Bayesian decision theory and classifiers1
    CET792 ML2: Supervised learning: Non-probabilistic classifiers1
    CET793 ML3: Unsupervised learning1
    CET794 AI Ethics 1: Foundations of AI Ethics1
    CET795 AI Ethics 2: AI Ethic Standardization
    CET802 DS1: Descriptive Analytics
    CET804 DS3: Pattern Recognition1
    CET805 AI Ethics 3: Ethics in Data Processing1
    CET806 Application 1: Introduction to Affective AI1
    CET807 Application 2: Introduction to Computer Vision 1
    CET808 Application 3: Introduction to Cloud AI1

    Listed courses are:

    • Credit-bearing and stackable to Graduate Certificate in Data Science and Artificial Intelligence (total 9AUs), and FlexiMasters in Data Science and Artificial Intelligence (total 15AUs).
    • SSG funded and SkillsFuture Credit approved.