MRes Experimental Psychology with Data Science / Course details
Year of entry: 2024
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Course unit details:
Scientific Programming, Computational Tools and Machine Learning
Unit code | PCHN63162 |
---|---|
Credit rating | 15 |
Unit level | FHEQ level 7 – master's degree or fourth year of an integrated master's degree |
Teaching period(s) | Semester 2 |
Available as a free choice unit? | No |
Overview
The topics covered will include:
- Using Conda to create reproducible Python environments
- Programming in Python using Jupyter Notebooks
- Data analysis using the NumPy, pandas (and related) Python libraries
- How to use git and GitHub for version control and collaborative working
- Making your analyses reproducible using Docker
- Introduction to MATLAB programming
- Advanced data analysis in MATLAB
- Understand the principles of statistical/machine learning
- Classification and resampling methods
- Model selection and regularization
- Unsupervised statistical learning
Aims
- To equip students with a range of advanced computational and analytical techniques.
- To equip students with the confidence and skills necessary to apply the methods to datasets using Python, MATLAB and R.
- To provide sufficient understanding for sophisticated statistical decision-making and interpretation of results.
- To contextualise statistical analysis in the context of the principles of reproducibility and Open Research.
Learning outcomes
Having attended the unit, students will be able to:
- conduct data analyses in Python and MatLab
- demonstrate their knowledge and skills required for open and reproducible science.
- demonstrate their ability to understand the principles of statistical/machine learning.
- demonstrate their ability to apply statistical learning methods on contextualised datasets.
Teaching and learning methods
In Semester 2 there will be weekly 2-hour seminars providing an introduction and explanation of each technique and practical training. Each technique will be demonstrated using Python, MatLab, R or other appropriate software.
Assessment methods
Method | Weight |
---|---|
Other | 50% |
Set exercise | 50% |
Continuous assessment. Two assignments. Each topic will be formally assessed by a written assignment, worth 50% of the marks for this module. One assessment will be on programming and/or data analysis using Python (1000 words equivalent) and the other assessment will be on applying statistical learning techniques to existing data (1000 words equivalent).
Recommended reading
Appropriate online resources will be made available alongside each lecture.
Study hours
Independent study hours | |
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Independent study | 150 |