|Dr Ben Youngman
When we want to fit a statistical model to some data it is almost inevitable that a computer will make this process much easier. Computers can speed up calculations, avoid the tedium or potential for error of doing calculations by hand, and have allowed us to analyse amounts of data and fit new models that were simply impractical without them. Data Science is built on the fitting of statistical models to data. While such models continue to evolve, we must balance what's theoretically and practically possible; otherwise we have data that we can't analyse and models that we can't estimate. We can achieve more by fitting statistical models efficiently.
To efficiently fit statistical models, we often use fundamental mathematical concepts, including some that you will have previously seen, such as matrix decompositions. You will learn a variety of these concepts from the theory behind them to their role in analysing real-life data. You will see some important statistical models that rely on these concepts and how the R programming language can be used for computation, in particular some of its more advanced features for calculations and analysing data. You will gain experience in programming while learning new statistical methods and models, through interesting examples and exercises. After this module you will be able to analyse more complex data with more advanced statistical techniques.
Please note that all modules are subject to change, please get in touch if you have any questions about this module.