Module overview
This module aims to teach students the fundamentals of writing structured computer programs, applicable using any high level programming language. However, students will be shown the special features of Python that makes this language especially useful for Data Science and Decision Science. The module uses software engineering techniques to enforce the importance of good programming practise and will review traditional computing algorithm analysis, design and implementation using Python.
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- demonstrate knowledge and understanding of traditional computing algorithm analysis, design and implementation using Python.
- programme according to good practice, applicable in all high level languages.
- carry out analysis, design and implementation of algorithms using Python.
- demonstrate skills in technical report writing.
- demonstrate a working facility of using Python.
- demonstrate knowledge and understanding of the fundamentals of writing structured computer programs, applicable to using any high level programming language.
- access your own library of algorithms for use in other modules or in project work.
- write structured computer programs, applicable using any high level programming language.
Syllabus
No prior programming experience is required. The module will cover the basic principles of programming in a high level language. The main focus will, however, be in developing a working facility of Python. The module will cover a range of the most commonly used techniques and algorithms including technical calculations as well as data manipulation, graphics, file handling, and the use of package extensions such as numpy. Practical exercises are used to reinforce the ideas taught in the module, which will enable the students to build up their own library of algorithms for use in other modules or in project work.
Learning and Teaching
Teaching and learning methods
The module will be taught using a mixture of lectures and computer workshops: 18 hours of computer workshops; 6 hours of lectures.
| Type | Hours |
|---|---|
| Independent Study | 51 |
| Teaching | 6 |
| Workshops | 18 |
| Total study time | 75 |
Resources & Reading list
Textbooks
Phuong Vothihong, Martin Czygan, Ivan Idris, Magnus Vilhelm Persson & Luiz Felipe Martins. Python: End to End Data Analysis. Packt Publishing.
DE Knuth. The Art of Computer Programming (Volume 1).
Benjamin Baka. Python Data Structures and Algorithms. Packt Publishing.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Coursework assignment(s) | 100% |
Repeat Information
Repeat type: Internal & External