This module provides foundational knowledge in Python programming for data science, covering basic syntax, data types, and essential libraries for data manipulation, analysis, and visualization.

This module covers essential statistical concepts and probabilistic methods for data analysis, including distributions, central tendency measures, correlation analysis, and inferential statistics for making data-driven decisions.

This module focuses on practical data preparation techniques and visualization skills using Python, covering real-world data cleaning methods and effective presentation of data insights through visual representations.

This module provides comprehensive coverage of fundamental machine learning algorithms and techniques, including mathematical foundations, supervised and unsupervised learning methods, and advanced feature engineering for building predictive models.

This module establishes the fundamental concepts of deep learning, covering neural network architectures, backpropagation, optimization techniques, and hands-on experience with popular deep learning frameworks for AI applications.

This module explores advanced deep learning architectures and their practical applications across various domains including computer vision, natural language processing, and healthcare, covering specialized techniques like transfer learning and object detection.

This module introduces the operational aspects of machine learning and large language models, covering deployment, monitoring, and management principles for maintaining ML models in production environments.

This module provides insights into cutting-edge AI applications across diverse industries and emerging technologies, covering cybersecurity, supply chain optimization, generative AI, environmental monitoring, and cloud-based AI solutions.

This capstone module integrates all learned concepts through an end-to-end AI project, covering the complete machine learning lifecycle from data preprocessing to model deployment while incorporating generative AI components to solve real-world problems.

This module covers the essential tools and platforms used throughout the programme for hands-on learning, including cloud-based development environments, programming languages, visualization libraries, and deep learning frameworks.