This course offers secondary school students a comprehensive introduction to the fields of Data Science and Artificial Intelligence (AI). Through interactive lessons and hands-on projects, students will learn the fundamentals of data collection, analysis, and visualization, along with core AI concepts such as machine learning, natural language processing, and computer vision. The curriculum emphasizes practical understanding, using accessible tools and real-world datasets to solve problems and make data-driven decisions. Ethical considerations and the societal impact of AI and data science are also explored. By the end of the course, students will have a solid foundation in both Data Science and AI, preparing them for further study in these exciting and rapidly evolving fields.

This course introduces secondary school students to Python, a versatile programming language. Students will learn the basics of Python, including installation, running scripts, and using Jupyter Notebook. The course covers fundamental programming concepts such as variables, data structures, loops, conditional statements, and file input/output operations. Additionally, students will learn how to install and use Python libraries, with a focus on pandas for data manipulation and analysis. Through engaging lessons and hands-on projects, students will build practical coding skills and be prepared for more advanced programming challenges.

This course offers secondary school students a comprehensive introduction to Machine Learning (ML) within an AI and Data Science framework. Students will learn the fundamentals of ML, including supervised learning techniques such as classification and prediction, unsupervised learning methods, and reinforcement learning principles. The course emphasises practical applications of ML, providing students with hands-on experience in solving real-world problems using these techniques. By the end of the course, students will have a solid understanding of ML concepts and how to apply them in various data science contexts.

This course introduces secondary school students to the essentials of Deep Learning (DL) as part of an AI and Data Science program. Students will start with the basics of DL and neural networks, then dive into the Python tools and libraries used for deep learning, including TensorFlow. The course covers practical applications such as classification with deep learning, as well as real-world uses like image classification, object detection, and text processing. Through hands-on projects and exercises, students will gain valuable experience in developing and applying deep learning models to various data challenges.

This course introduces secondary school students to the fascinating field of Generative AI. Students will explore the core concepts of generative models, including how they create new content such as images, text, and music. The course covers foundational techniques and algorithms used in Generative AI, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Through hands-on projects and practical examples, students will learn how to build and apply generative models to create innovative solutions and creative applications. By the end of the course, students will have a solid understanding of Generative AI and its potential impact on various fields.

This course introduces secondary school students to Python, a versatile programming language. Students will learn the basics of Python, including installation, running scripts, and using Jupyter Notebook. The course covers fundamental programming concepts such as variables, data structures, loops, conditional statements, and file input/output operations. Additionally, students will learn how to install and use Python libraries, with a focus on pandas for data manipulation and analysis. Through engaging lessons and hands-on projects, students will build practical coding skills and be prepared for more advanced programming challenges.

This course introduces secondary school students to Python, a versatile programming language. Students will learn the basics of Python, including installation, running scripts, and using Jupyter Notebook. The course covers fundamental programming concepts such as variables, data structures, loops, conditional statements, and file input/output operations. Additionally, students will learn how to install and use Python libraries, with a focus on pandas for data manipulation and analysis. Through engaging lessons and hands-on projects, students will build practical coding skills and be prepared for more advanced programming challenges.

This course offers secondary school students a comprehensive introduction to Machine Learning (ML) within an AI and Data Science framework. Students will learn the fundamentals of ML, including supervised learning techniques such as classification and prediction, unsupervised learning methods, and reinforcement learning principles. The course emphasises practical applications of ML, providing students with hands-on experience in solving real-world problems using these techniques. By the end of the course, students will have a solid understanding of ML concepts and how to apply them in various data science contexts.

This course introduces secondary school students to the essentials of Deep Learning (DL) as part of an AI and Data Science program. Students will start with the basics of DL and neural networks, then dive into the Python tools and libraries used for deep learning, including TensorFlow. The course covers practical applications such as classification with deep learning, as well as real-world uses like image classification, object detection, and text processing. Through hands-on projects and exercises, students will gain valuable experience in developing and applying deep learning models to various data challenges.

This course introduces secondary school students to the fundamentals of Big Data Analytics (BDA) within a Data Science program. Students will begin with an overview of BDA and its significance in handling large datasets. The course covers various types of data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics. Students will learn to process big data using PySpark, a powerful tool for large-scale data processing, and explore MongoDB and PyMongo to work with NoSQL databases. Through practical exercises and projects, students will develop the skills to analyse and interpret big data effectively.