# Statistical Computing 2

This set of portfolios contains the notes that were submitted for the second statistical computing module in the COMPASS CDT.

*To view these portfolios, please see the menu bar on the left. You may have to scroll down.*

In general, these notes form more advanced statistical computation methodologies, using a lower level language than R: C++. C++ is beneficial for its speed advantages over interpreted languages such as R or Python. This makes it a good candidate for computationally intensive methods such as Bayesian MCMC sampling, or when working with big data.

Most of my statistical work has been using R, so this module was a good opportunity for me to learn C++, and develop my Python skills. In general, I still prefer R to Python for basic data analysis for its simplicity, but there are a lot of cases (such as neural networks in TensorFlow) where Python is superior. It is important to be able to know both.