Numerical Analysis

Learning Objectives

• Develop a systematic understanding of foundational mathematical principles and methods, as well as core and specialised concepts underpinning computing logic.
• Understand the foundation for the development and application of programming and data-driven techniques, from both a theoretical and practical viewpoint.
• Facilitate the ability to interpret the results generated when using these data science and ai tools.
• Gain an understanding of the real-world applications of these computational tools, and contemporary issues related to these computational techniques.
• The opportunity to take a reflective and independent approach to the learning process.
• Demonstrate systematic understanding of the key mathematical and statistical concepts and techniques which underpin mechanisms in Data Science and AI.
• Apply mathematical and statistical methods in these fields to help in the decision-making process.
• Critically evaluate the use of statistical analysis and the numeric interpretation of results as aids in the decision-making process.

Module Notes and Assignments

Throughout the module, we engaged in a series of formative tasks and assessments, aimed at charting our progress. I committed myself fully to these activities, and to ensure my work was easily accessible, I uploaded everything to my GitHub repository. Additionally, I created detailed revision notes for various units within the module. These efforts were not just to keep track of my progress but also to deepen my understanding and improve my overall performance in the course.

My module notes, formative activities, and Assessment submissions can be accessed here