AI, ML and Data Science Programme

The programme learning will pause for holidays during Christmas (24 Dec 2025 – 1 Jan 2026) and Chinese New Year (16 Feb – 22 Feb 2026).

In today’s data-driven world, organisations rely on vast amounts of information to guide every decision, process, and strategy. From data analytics to advanced machine learning (ML) and generative AI (GenAI), the AI, ML and Data Science Programme by NUS School of Computing equips professionals with the essential skills to transform raw data into actionable business insights.

The programme places a strong emphasis on real-world relevance, including live masterclasses, hands-on exercises, capstone projects and emerging tools to ensure you are equipped with market-ready skills. By the end of the journey, you will be able to design, implement and optimise AI-powered systems to enhance decision-making, automate processes and drive competitive advantage.

  This comprehensive programme combines three key pillars:  

• Data Analytics – Data to Insights: Learn to process, clean, visualise and analyse data using tools such as Power BI, Power Pivot and Orange, building a strong foundation in descriptive statistics, database querying and data mining techniques.

• Programming with Python: Gain hands-on expertise with NumPy, Pandas, Scikit-Learn, Keras, TensorFlow and more to manipulate, model and deploy data-driven solutions.

• Machine Learning, AI & GenAI Applications: Explore supervised and unsupervised learning, neural networks, reinforcement learning, deep learning, recommendation systems, Machine Learning Operations and Generative AI with Hugging Face and OpenAI Playground to create scalable and intelligent solutions.

• Data-Driven Decision-Making with Statistical and Analytical Techniques - Gain foundational knowledge in statistics and analytics to extract meaningful insights from raw data and support strategic business decisions.

• Database Management and Modelling with DB Browser for SQLite, Power BI Desktop and Excel - Build, clean and manipulate data using industry-standard tools. Create relational databases, construct data models and prepare datasets for visualisation and analysis.

• Python for Data Science: End-to-End Data Workflows - Use Python with core libraries like NumPy, Pandas and Scikit-Learn to automate data collection, cleaning, transformation, analysis and modeling for scalable solutions.

• Applied Machine Learning: Algorithms and Use Cases - Implement supervised and unsupervised machine learning algorithms including classification, regression, clustering and association rule mining. Understand model evaluation and optimisation for real-world problems.

• Deep Learning with TensorFlow and Keras - Design, train and optimise deep learning architectures such as neural networks and CNNs. Use TensorFlow and Keras to solve complex tasks like image recognition, sentiment analysis and more.

• Operationalising AI: Machine Learning Operations and Model Lifecycle Management - Deploy and manage AI models at scale using Machine Learning Operations principles. Leverage tools like MLflow and version control to track experiments, manage reproducibility and streamline model updates and deployment.

• Recommendation Engines and Reinforcement Learning - Build and evaluate recommendation systems using collaborative filtering, content-based filtering and hybrid approaches. Explore reinforcement learning to develop models that learn from dynamic environments and feedback loops.

• Generative AI and Natural Language Processing (NLP) - Explore the power of generative AI and NLP with tools such as OpenAI Playground, Hugging Face and Transformer-based models. Apply these technologies to a wide range of language and AI applications.

• Responsible AI - Explainability, Fairness and Governance - Ensure transparency, fairness and accountability in AI models. Apply responsible AI principles to enhance explainability, mitigate bias and uphold ethical standards in AI deployment.

The programme is designed for professionals without prior coding experience, from both tech and non-tech domains, who want to:

• Pivot into a Machine Learning career

• Acquire Machine Learning concepts and techniques to transition into Machine Learning roles

• Gain the expertise to lead and solve real-world business problems with data and modelling at the core

The programme is particularly applicable to major industries such as IT products and services, banking and financial services, consulting, education, healthcare and retail.

Stepping into a business leadership career requires a variety of job-ready skills. Below given services are provided by Emeritus, our learning collaborator for this program. The primary goal is to give you the skills needed to succeed in your career; however, job placement is not guaranteed.

Emeritus provides the following career preparation services:

• Resume building videos

• Interview preparation videos

• Linkedln profile building videos

• Glossary of resume templates

Please note:

NUS or Emeritus do not promise or guarantee a job or progression in your current job. Career Services is only offered as a service that empowers you to manage your career proactively. The Career Services mentioned here are offered by Emeritus. NUS is not involved in any way and makes no commitments regarding the Career Services mentioned here

What is it like to learn with the learning collaborator, Emeritus?

More than 300,000 professionals globally, across 200 countries, have chosen to advance their skills with Emeritus and its educational learning partners. In fact, 90 percent of the respondents of a recent survey across all our programs said that their learning outcomes were met or exceeded. All the contents of the course would be made available to students at the commencement of the course. However, to ensure the program delivers the desired learning outcomes, the students may appoint Emeritus to manage the delivery of the program in a cohort-based manner during the course period the cost of which is already included in the overall Course fee of the course.

A dedicated program support team is available 7 days a week to answer questions about the learning platform, technical issues, or anything else that may affect your learning experience.