Module Catalogue 2024/25

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

Aims

The aims of this module are to:

•       Provide students with a foundational understanding of key computing concepts and principles, emphasising their application through mathematical methods.

•       Develop students' ability to apply mathematical methods in algorithmic problem-solving, emphasizing the role of logic and mathematical structures.

•       Introduce students to programming fundamentals using Python, focusing on mathematical applications, data representation, and algorithm implementation.

•       Cultivate critical thinking skills by exploring the integration of mathematical reasoning in computational contexts, fostering an understanding of mathematical models and their applications.

•       Raise awareness about the ethical and social implications of computational mathematics including considerations related to data ethics, privacy, and responsible use of mathematical models.

Outline Of Syllabus

This course will include the following materials:

1.       Introduction to Computing.      

2. Fundamental Mathematical Concepts for Computing including algebraic structures, discrete mathematics, calculus in computing.

3.       Algorithmic Thinking with Mathematical Models including translating mathematical concepts into algorithms, analysing and designing algorithms.

4.       Python Programming Basics including Introduction to Python, Python syntax and data types, Control structures in Python, Functions, Classes, Modules and Packages.

5.       Python Libraries for Mathematical Computing including Introduction to NumPy and SciPy, Numerical computation and linear algebra with Python

6.       Symbolic Mathematics in Python including Introduction to SymPy, Symbolic computation and algebraic manipulation.

7.       Real-world Applications of Computational Mathematics including Case studies in scientific computing.

8.       Ethical and Social Considerations in Computational Mathematics including Data ethics, Privacy considerations, Responsible use of mathematical models.

Intended Knowledge Outcomes

On completing this module, students will be able to demonstrate knowledge and understanding of:

IKO1 - Computing Concepts including algorithms, data representation, and computer architecture.

IKO2 - algebraic structures, discrete mathematics, and calculus in the context of computing.

IKO3 - applying algorithmic thinking to solve computational problems, translating mathematical models into executable code.

IKO4 - Python programming basics

IKO5 - applying Python for numerical computing using libraries such as NumPy and SciPy

IKO6 - utilising Python's SymPy library for symbolic mathematics and algebraic manipulation

IKO7 - applying critical thinking skills to integrate mathematical reasoning into computational problem-solving.

IKO8 - Implementing algorithms in Python and analyse their efficiency and correctness.

IKO9 - ethical considerations related to data, privacy, and responsible use of mathematical models.

Intended Skill Outcomes

Upon completing the module students will be able to:

ISO1 - Reflect on the learning process, challenges, and experiences, as evidenced in portfolio reflections.

ISO2 - apply computational mathematics to real-world applications in various domains, such as science, engineering, and finance.

ISO3 - apply computational methods to analyse and address real-world challenges.

ISO4 - break down complex problems into smaller, solvable components using algorithmic thinking.

ISO5 - Implement algorithms in Python with a focus on efficiency, correctness, and understanding algorithmic complexity.

ISO6 - Apply critical thinking skills to integrate mathematical reasoning into the analysis and solution of computational problems.

Teaching Rationale And Relationship

Lectures serve as the foundation for conveying theoretical concepts, historical perspectives, and fundamental principles of computing. Through lectures, students gain a broad understanding of the evolution of computing, key mathematical foundations, and the theoretical underpinnings of algorithms, et al.


Practical sessions, conducted in a PC cluster, provide students with hands-on experience in applying theoretical knowledge to solve real-world problems. These sessions involve coding exercises, and the practical application of Python programming and mathematical methods.

Assessment Rationale And Relationship

Portfolio-based assessment shifts the focus from traditional exams to a continuous evaluation approach. This method allows students to showcase a diverse range of skills and reflections, providing a holistic view of their learning journey.

Portfolio-based assessment supports continuous learning and reflection by encouraging students to compile evidence of their progress, challenges, and achievements. It assesses content understanding, programming skills, critical thinking, and communication skills through a variety of artifacts.

The inclusion of real-world applications showcases the practical relevance of computing and mathematical methods. This method enhances student motivation and helps bridge the gap between theoretical knowledge and its application in various domains.

General Notes

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