Data and AI sciences
300+ expert-led courses spanning data engineering, machine learning and applied AI — Python, R and project-based labs from publishers like Packt, Wiley and Mercury Learning.
Overview
Teach data engineering, machine learning and applied AI from one pathway built for US computer science programmes. The catalogue runs from the foundations of data engineering and statistical analysis through to deep learning, natural language processing, computer vision and responsible AI. Students write code in Python and R and work in PyTorch, TensorFlow and scikit-learn against real datasets and project-based labs, so what they learn in class matches what they will do in a data team.
The content comes from recognised technical publishers such as Packt, Wiley and Mercury Learning, written by working data scientists and engineers whose books already sit on practitioners' shelves. Faculty get coding projects, case studies and teaching resources that make it straightforward to keep a course current as the field moves. The pathway suits undergraduate and graduate programmes, maps to the industry credentials employers look for, and prepares students for research and applied roles across data-driven sectors.
Curriculum
- Foundations of Data Engineering
- Applied Machine Learning with Python
- Deep Learning and Neural Networks
- Natural Language Processing
- Responsible AI and Ethics
Learning outcomes
- Build and manage scalable data pipelines
- Apply statistical methods to real-world datasets
- Train and evaluate predictive models
- Understand and apply ethical principles in AI