This curriculum is designed to provide a comprehensive foundation in data science, with a focus on programming, mathematical techniques, and machine learning methods. "Programming for Data Science" equips students with the essential coding skills necessary for handling, processing, and analyzing large datasets, using languages and tools commonly employed in the field. "Applicable Maths" covers the mathematical concepts crucial for understanding data science techniques, ensuring students are well-prepared to work with statistical models and algorithms. "Exploratory Data Analytics and Visualization" teaches how to explore datasets and present insights effectively through visual tools, enabling students to make sense of data before applying more complex models. "Supervised Learning" delves into machine learning algorithms that rely on labeled data, focusing on training models to make predictions or classifications. "Ethical Machine Learning and Data Science (Part 1)" emphasizes the ethical considerations in data science, ensuring that students understand the impact of their work on society and are equipped to create fair and responsible AI systems. "Unsupervised Learning" explores methods for discovering patterns and relationships in unlabeled data, while "Bayesian Methods and Computation" introduces students to probabilistic models and Bayesian inference, providing a solid framework for statistical decision-making and prediction. Together, these courses cover the essential techniques and ethical principles needed to succeed in data science and machine learning.

This advanced curriculum focuses on sophisticated techniques for analyzing complex data, developing intelligent systems, and addressing the ethical implications of machine learning. "Unstructured Data Analysis" teaches methods for handling and extracting meaningful information from unstructured data sources such as text, images, and audio, equipping students with the tools necessary to deal with diverse data types. "Learning Agents" delves into the development of autonomous systems capable of learning from their environment, applying reinforcement learning and other approaches to create intelligent agents that improve their performance over time. "Ethical Machine Learning and Data Science (Part 2)" builds on earlier coursework, deepening students' understanding of the ethical challenges in AI and machine learning, with a focus on fairness, transparency, and accountability in model development. "Deep Learning" introduces students to advanced neural network architectures, enabling them to work on cutting-edge problems such as computer vision and natural language processing. "Big Data: Statistical Scalability with PySpark" focuses on handling large-scale datasets, teaching students how to use PySpark for distributed computing and scalable data processing. Finally, the "Research Project" allows students to apply their knowledge to real-world problems, conducting independent research and contributing to the field of data science with practical solutions. Together, these courses provide an in-depth exploration of the technical, ethical, and practical aspects of modern data science and machine learning.