In term 1 you will develop your core data scientific knowledge and skills. It is divided into three compulsory modules which span the breadth of data science, from the fundamentals of statistics and programming in Python and R, to modern machine learning and artificial intelligence. These core modules provide a strong foundation for the specialist pathways in term 2.
This module provides a practical introduction to statistical learning from model training to deployment. The student will learn about optimisation as a means for model training; the statistical principles and methods that underpin model selection, and the application of classification and regression models to real data problems.
A variety of supervised learning models and their estimation will be covered, including linear models and their connection to kernel based methods; feed-forward neural networks; and tree based models. Tree based models will be extended to their use in random forests and gradient boosted models. In addition topics relevant to big-data problems including dimensionality reduction variable selection; and stochastic gradient descent will be covered.
Students will be introduced to the widely-used statistical computing package R, which will be the primary tool for data analysis and modelling in this module. In addition to learning how to use R effectively and efficiently, students will also be encouraged to compare and contrast with their existing or developing knowledge of general-purpose languages such as Python.
The main goal of this module is to explore the essence of AI and Data Science, their origins, and their roles in solving real-world challenges. You'll delve into the duties and skills of data professionals, emphasising effective communication and ethical considerations. The module also covers the legal and societal impacts of AI, while promoting teamwork through hands-on projects that tackle AI and Data Science challenges. Supported by industry talks, you'll learn to formulate problem statements, select appropriate methods, and communicate findings effectively, preparing you for a successful career in this dynamic field.
The main goal of this module is to equip you with essential Python programming skills and foundational mathematical concepts crucial for AI and Data Science. Through hands-on learning, you'll develop the ability to solve real-world problems, process complex data sets, and apply key mathematical techniques like probability and matrix operations. Formative assessments will support your learning, leading to a final practical assessment that prepares you for advanced studies. Perfect for those new to computing or mathematics, this module sets the stage for your success in AI and Data Science.
