AI, ML and Data Science Programme

Module 1 - Introduction to Analytics

• The role of analytics in business intelligence and artificial intelligence

• Data processing chain

• A simple analytics example

Module 2 - Mathematics Fundamentals

• Descriptive statistics & data distributions

• Different datatypes attributes

• Percentages, ratios & growth metrics

• Understanding relationships: correlation & causation

• Basic probability concepts

Module 3 - Database: Data Source and Data Queries

• Understanding databases structure

• Exploring a database in SQLite

• Getting started with queries using SQLite

• Turning data into information

• Working with multiple tables

• Using functions

Module 4 - Data Warehouse: Load and Transform Data and Create a Data Model

• Data preparation and data wrangling

• Introduction to Exploratory Data Analysis (EDA)

• Exploring and importing data into Power Pivot

• Data munging with Power Query

• Star schema data model

• Creating a data model in Power Pivot

Module 5 - Data Visualisation: Pivot Tables and Charts and Power BI Basics

• Working with Pivot tables and charts to create visualisations

• Creating the BI interface in Excel

• Case study using Power Pivot: Reseller Sales Analysis

• Creating data Model, visualisations and dashboards in Power BI

• Case study using Power BI: Sales Quota Analysis

• Common visualisations used in EDA

Module 6 - Data Mining Basics

• The fundamentals of data mining and CRISP-DM

• Demonstrating unsupervised learning technique, specifically, K-Means clustering in Orange using practical examples

• Demonstrating supervised learning techniques such as decision tree and linear regression models in Orange to solve real-life problems

Online Live Session by Faculty: Masterclass - AI in Data Analytics

Module 7 - Getting Started with Python

• Introduction to Python as a programming language

• Identifying types of errors in a Python program

• Examining the basic building blocks in a Python program

• Creating a simple Python program

Module 8 - Collections, Strings and Comments

• Using appropriate containers or collection data types to store data

• Applying properties of lists, tuples, sets and dictionaries to organise data

• Manipulating data and containers using functions and built-in methods on collections

• Identifying the characteristics of strings

• Implementing functions, operators and built-in methods on strings

Module 9 - Operators and Program Flow Controls

• Explaining concept of various operators

• Using operators under appropriate conditions

• Applying operator precedence and associativity for expression resolution

• Examining the different types of program flow control

• Utilising appropriate program flow control mechanisms

• Executing sequence, selection and iteration program flow controls

Module 10 - Functions, Import Statements, Inputs, Outputs, Exception Handling and File Handling

• Exploring different types of functions

• Differentiating functions from methods

• Applying import keywords to Python modules

• Applying the correct input syntax

• Using exception statements to handle code errors

• Displaying outputs with the correct syntax and format

• Exploring methods in handling text and binary files

Module 11 - The Numpy Library

• Applying concepts in indexing, slicing and iterating arrays

• Understanding the process of reading and writing arrays into files

• Exploring the methods to manipulate arrays

Module 12 - The Pandas Library

• Creating and manipulating series and dataframes in the pandas library

• Applying operations and functions to manipulate and manage values

Module 13 - Scikit Learn and Keras

• Overview on Scikit learn

• Classification, regression, clustering, model selection, pre-processing and dimensionality reduction in Scikit Learn

• Importing the library along with various functions required for model evaluation- confusion matrix, test_train_split etc.

• Overview on deep learning

• Types of models supported by Keras

• Importing the library and building models in Keras and its applications

Module 14 - Data Visualisation

• Creating data visualisations in matplotlib, pandas plot and Seaborn library

• Applying concepts in enhancing and manipulating data visualisations

Module 15. Python Application Development

• Applying concepts in Python to create an Inventory Management System

• Building a Python application using external data

Online Live Session by Faculty: The application of concepts learned, doubt clarification, summary of overall section

Module 16 - Introduction to Machine Learning and Artificial Intelligence

• Defining artificial intelligence, machine learning and data science and their correlation

• The history and applications of AI and machine learning

• Future trends and approaches in the AI field

• Learning patterns of machines

• The machine learning process and approaches

• Concepts of learning difficulties and lack of performance

• Underfitting and overfitting

Module 17 - Supervised Learning: Classification

• Applications of classification in business scenario

• Explanation of dependent variable in classification

• Continuous dependent variable versus categorical variable

• Machine Learning techniques to predict the classes in classification

• Demonstration using logistic regression in Python

• Discussion of model statistics to evaluate the model

Module 18 - Supervised Learning: Regression

• Regressive algorithms for implementing supervised learning with examples

• Case study discussion: The use of supervised learning, specifically linear regression in NBA teams to improve their ranking and win a cup

• Regressive models such as function estimation as linear regression and classification using logistic regression

• The role of regressive models in function estimation and classification

Module 19 - Supervised Learning: Decision Trees

• The theory, terminologies and applications of decision trees

• The random forest ensemble system for high-performance decision-making and its applications

• Machine learning concepts such as the entropy, GINI index and imbalanced classes issues

Module 20 - Support Vector Machine Algorithms

• Defining support vector machine (SVM) and explaining the underlying algorithm

• Discussing parameter tuning in SVM

• Demonstrating SVM for classification and regression

• Advantages and disadvantages of using SVM

Module 21 - Unsupervised Learning: Clustering techniques in K-Means

• Describing the K-means clustering algorithm

• Developing and using the K-means algorithm using Scikit Learn

• Optimising a clustering system by changing the clustering parameters

• Visualising the results of clustering

Module 22 - Unsupervised Learning: Hierarchical Clustering Method

• Different applications of hierarchical clustering

• The importance of metrics and linkage criteria in hierarchical clustering

• Top-down and bottom-up approaches of hierarchical clustering

• The implementation of hierarchical clustering and interpretation of results

Module 23 - Unsupervised Learning: Probabilistic and Association Rule

• Overview of probabilistic clustering and comparison with K-means

• Exploring the Gaussian Mixture Model and Expectation Maximisation algorithm

• Overview of association rule and its application

• Applying pre-processing and Apriori algorithm

• Demonstration of association rule for market basket analysis

Module 24 - Neural Network

• The theory, terminology and definition of artificial neural networks

• Training algorithms of neural networks

• Neural network parameters and ways to prevent underfitting and overfitting

• Identifying a multi-layer perceptron as a solution to the problems of nonlinearity

• The implementation of some machine learning applications in the system

• Developing a model of biological brain as an artificial neural network

• Examples of how to use neural networks as a function estimator

• Setting optimal parameters to improve performance

Module 25 - Reinforcement Learning

• Definitions and applications of reinforcement learning

• Types of reinforcement learning

• Reinforcement learning examples, such as traffic light control and personalised recommendation systems

• Robot navigation using reinforcement learning

Module 26 - Deep Learning Using Keras and Tensorflow

• Defining deep learning and its applications

• Examining how deep neural networks deal with semi-structured and unstructured data

• Explain types of networks and its applications

• Exploring backward and reverse propagation

• Describing layers in sequential neural net-hidden layers, input and output layers

• Describing hyperparameters in neural net

• Demonstration and application of deep learning techniques

Module 27 - Deep Learning: Advanced Topics

• Model compression & quantisation techniques

• Advanced Deep Convolutional Models

• Introduction to GANs (Generative Adversarial Networks)

• Self-supervised learning techniques

• MLflow for experiment tracking and model lifecycle management

Module 28 - Recommendation Systems

• Defining recommendation systems and its uses

• Exploring types of recommendation systems and their advantages and disadvantages

Module 29 - MLOps and Model Deployment

• Model monitoring and logging in production

• Deploying reinforcement learning models in real-world environments

• Version control for machine learning models

• Automated model retraining pipelines

• Scaling deep learning models for production

• Deploying recommendation systems at scale

• A/B testing and monitoring recommendation models

Online Live Session by Faculty: Deep learning and recommended system applications and tools

Module 30 - Gen AI and Prompt Engineering

• Introduction to Generative AI

• Understanding LLMs (Large Language Models) and their architecture, Agentic AI

• Prompt Engineering: Crafting effective AI prompts

• Hands-on with OpenAI Playground/APIs

Online Live Session by Faculty: Suggested topic - Agentic AI UseCases

Module 31 - Natural Language Processing

• Introduction to NLP and Hugging Face

• Text preprocessing, tokenisation, and word embeddings

• Named Entity Recognition (NER), Sentiment Analysis

• Fine-tuning pre-trained models for NLP tasks

• Hands-on with Transformers and pre-trained LLMs (BERT, GPT-3)

Module 32 - Advanced AI & Future Trends in AI

• AI-powered AutoML & Neural Architecture Search (NAS)

• Hands-on with state-of-the-art models in 2025

• Ethical concerns & AI governance frameworks

• Explainable AI (XAI) techniques: SHAP, LIME, and model interpretability

• Understanding Bias & Fairness in ML Models

Online Live Session by Faculty: Suggested topic - Future of AI: Quantum & Responsible AI

The curriculum covers topics such as python, machine learning, data analytics, python programming, python for data analytics, python for machine learning, amongst others.

You will work with tools like SQLite, PowerPivot, Orange Data Mining and more. Complete a capstone project applying AI with deep learning techniques to solve industry challenges.

The programme is delivered online with live sessions from NUS School of Computing faculty and industry experts. Pre-recorded videos allow flexible learning, while live sessions enable real-time interaction.

Expect 6–8 hours weekly over 8 months, including video lectures, assignments, and live discussions.

Yes, the programme offers flexible payment options. You can choose to pay the full fee upfront or spread the cost over two or three instalments, making it easier to manage your finances.

Yes, there's a 1-day campus immersion (optiona) for networking with peers and faculty members.

Upon successful completion of the programme, participants will be awarded a verified digital certificate by NUS School of Computing.

Access the platform and resources for 12 months post-programme to revisit the content and stay updated.