PY2SCP (Scientific Computing for Psychologists) - Spring
Module aims and purpose
Students will be introduced to the basics of scientific computation in Python for data analysis visualisation and programming of behavioural experiments1. This will build upon and extend the neuroscience methods1 introduced in the prerequisite module PY1INM. Examples and problems will be drawn from a diverse range of areas from neuroscience, psychology, psychophysics and neuroimaging2. The foundational skill taught is programming (coding, scripting) in Python1 and will make use of appropriate widely used scientific libraries and packages e.g., PsychoPy for running behavioural experiments. All work will be carried out in the computer lab, with a strong focus on solving problems to acquire practical skills.
Module learning outcomes
By the end of the module, it is expected that students will be able to:
- Program in Python at a basic to intermediate level1
- Know how to use scientific libraries and packages in data analysis workflows in Python1
- Be competent at coding typical data visualisation tasks in Python1
- Understand and be able to program core elements of behavioural experiments in Python PsychoPy1
Summative assessment
| Type of assessment | Detail of assessment | % contribution towards module mark | Size of assessment | Additional information | Breadth | Depth |
|---|---|---|---|---|---|---|
| In-person in-class test | Programming and interpretation | 40 | 2 hours | Research Methods | ||
| In-person in-class test | Programming and interpretation | 60 | 2 hours | The second in-person in-class test is worth a higher percentage as students will have learnt more during the remainder of the teaching on the course, so the test assesses more advanced programming skills. | Research Methods |
