Principles and Applications of Data Science
By the end of this module students will gain experience of:
- How to explore the large datasets.
- How to prepare and clean large datasets for analysis (e.g. reformatting and pre-processing).
- How to wrangle data to obtain key exposures and outcomes
- How to visualise and present raw, intermediate and final datasets for effective communication of the final results.
- How to investigate different types of data using a machine learning framework
- How to develop code and analyses using reproducible methods.
- Overview of data science and AI/ML methods
- Processing phenotype data
- Data visualisation using R
- Reproducible research
- Introduction to unsupervised machine learning
- Processing a complex exposure