Module Information
Course Delivery
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | 1 Hours Online task in-class | 40% |
| Semester Assessment | 2 Hours Online coding test, in-class x 2 | 60% |
| Supplementary Assessment | As determined by the Departmental Examination Board | 100% |
Learning Outcomes
On successful completion of this module students should be able to:
Demonstrate an awareness of good practice when developing code.
Solve mathematical problems utilising small programs in Python.
Construct visualisations of data in Python.
Recognise when binomial, Poisson, uniform, or Gaussian distribution describes data, and calculate their mean, standard deviation, and other expectation values.
Show ability to analyse observational data, carry out error analysis, and perform hypothesis testing.
Brief description
There are numerous mathematical problems that are either impractical or impossible to solve analytically and must instead be solved by computers using numerical techniques. Mathematical techniques are essential to Physics and, therefore, so are such numerical techniques. This module introduces Python in the broader context of the Scientific Python Stack (Scientific Libraries/Extensions to the core Python language) and statistics. Application of these techniques will be achieved through practical workshops. This module is for physics students studying abroad in semester 2 of year 2.
Content
• Data structures: Lists, dictionaries and NumPy arrays
• Control Statements and Blocks: If-statements, for- and while-loop
• Organising Python code:
- Function definition and calling
- Catching and handling exceptions
- Organising code into modules
• File handling and data formats
• Visualising data (plotting) and data manipulation
• Statistics, including:
- Gaussian, Poisson and binomial distributions
- Hypothesis Testing
Module Skills
| Skills Type | Skills details |
|---|---|
| Application of Number | The application of number is required throughout the module. |
| Communication | Documenting Code. |
| Improving own Learning and Performance | From feedback (automatic, through computer, and in-practical feedback from demonstrators and staff). |
| Information Technology | Application of IT skills are central throughout the module. |
| Personal Development and Career planning | No, though skills taught are in high demand from employers. |
| Problem solving | Problem solving skills are required and developed throughout the module. |
| Research skills | Using a computer. Searching the language and library documentation. |
| Subject Specific Skills | Programming, debugging, statistical, and data analysis skills. |
Notes
This module is at CQFW Level 5