PCHN63162 15 Semester 2 Postgraduate Taught Level 7 Martyn Mcfarquhar Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 <p>The topics covered will include:</p><ul><li>Using Conda to create reproducible Python environments.</li><li>Programming in Python using Jupyter Notebooks.</li><li>Data analysis using the NumPy, pandas (and related) Python libraries.</li><li>How to use git and GitHub for version control and collaborative working.</li><li>Making your analyses reproducible using Docker.</li><li>Understanding the principles of statistical/machine learning.</li><li>Classification and resampling methods.</li><li>Model selection and regularization.</li><li>Unsupervised statistical learning. &nbsp;</li></ul> <p>The topics covered will include:</p><ul><li>Using Conda to create reproducible Python environments.</li><li>Programming in Python using Jupyter Notebooks.</li><li>Data analysis using the NumPy, pandas (and related) Python libraries.</li><li>How to use git and GitHub for version control and collaborative working.</li><li>Making your analyses reproducible using Docker.</li><li>Understanding the principles of statistical/machine learning.</li><li>Classification and resampling methods.</li><li>Model selection and regularization.</li><li>Unsupervised statistical learning. &nbsp;</li></ul> <ul><li>To equip students with a range of advanced computational and analytical techniques.</li><li>To equip students with the confidence and skills necessary to apply the methods to datasets using Python.</li><li>To provide sufficient understanding for sophisticated statistical decision making and interpretation of results.</li><li>To contextualise statistical analysis within the principles of reproducibility and Open Research.</li></ul> <p>Having attended the unit, students will be able to:</p><ul><li>Conduct data analyses in Python.</li><li>Demonstrate their knowledge and skills required for open and reproducible science.</li><li>Demonstrate their ability to understand the principles of statistical/machine learning.</li><li>Demonstrate their ability to apply statistical learning methods to different datasets.&nbsp;</li></ul> <p>The course will be taught through a combination of synchronous lectures and lab sessions, alongside online asynchronous teaching materials. Additional lab sessions and lab support will be available for students, if needed. Teaching will be complemented by the availability of notes, slides and recommended reading. &nbsp;</p> 0 Other 50% 9 Set exercise 50% <p>Continuous assessment. Two assignments. Each topic will be formally assessed by a written assignment, worth 50% of the marks for this module</p><p>One assessment will be on programming and data analysis using Python and the other assessment will be on applying statistical learning techniques to existing data&nbsp;</p> MRes Psychology MRes Exper Psyc with Data Sci PGDT Taught Component Mandatory N <p>Appropriate online resources will be made available alongside each lecture.</p> 0 0 150