SFY0025 : Introduction to Computing
- Offered for Year: 2024/25
- Module Leader(s): Dr Yongchang Pu
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
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.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 11:00 | 11:00 | Portfolio preparation |
| Scheduled Learning And Teaching Activities | Lecture | 11 | 1:00 | 11:00 | One hour lectures |
| Scheduled Learning And Teaching Activities | Practical | 10 | 2:00 | 20:00 | computer cluster practical session |
| Guided Independent Study | Independent study | 1 | 58:00 | 58:00 | Independent Study - Background reading and research to re-enforce knowledge and understanding |
| Total | 100:00 |
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 Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Portfolio | 1 | M | 100 | Portfolio based assessment |
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.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- SFY0025's Timetable