AI/ML & Data Science

Build practical capability in AI/ML & Data Science

Turn data into tested statistical and machine-learning analyses.

Program portfolio: Science & Innovation

Topics, tools and practical work

• Data cleaning and exploratory analysis
• Statistics and feature preparation
• Supervised and unsupervised learning
• Model evaluation and communication

Before you start

Basic Python and introductory statistics.

Tools to explore

Python; pandas; scikit-learn; Jupyter.

Example project

Build a prediction model with a reproducible notebook and model evaluation report.

Where the skills apply

Data analysis and applied machine learning.

Program planning

This is an indicative learning outline. Duration, teaching hours, delivery schedule, assessment, trainer and certificate requirements are confirmed for each individual learner or institutional cohort.

View the public Edxera program overview