My favorite tool for data analysis is the R programming language because it has a great community, it is free, and it works on Windows, Mac, and Linux. I also highly recommend RStudio if you’re not into using ESS with Emacs. RStudio is great for academics because it also serves as a LaTeX editor so you can use one tool for your entire research project soup to nuts. Microsoft offers a paid support option for commercial use but there is a free and open source version.
I learned R on my own but I could not have done it without the help of sites like Stack Overflow and the outstanding R community. Before posting any questions on the web, please attempt to work the problem yourself so that people don’t waste their time answering questions that have already been explained. More succinct: RTFM
Students: Download the pdf booklet PDQ Finance and follow the directions to install R and RStudio.
Non-Students: For minimal time investment, I suggest following some examples in the Getting Started in R (tinyverse edition) guide by Saghir Bashir and Dirk Eddelbuettel. I also suggest creating a free account at RStudio Cloud to get started without needing to install anything.
Installation and Introduction
- Command Line - some problems are best solved with command line tools rather than a programming language
- Emacs and ESS on OS X for Beginners
- Pandas - financial data analysis in Python
- Regression in R - short command for ordinary least squares regression
- Robust Regression in R
- Quantile Regression in R
- Time-Series Bootstrap
Data Manipulation & Presentation
More Advanced Stuff
- Style Analysis in R
- Fama-French Factor Model in R
- Pastor and Stambaugh Liquidity
- Working in Volume Time / Event Time
- VPIN in R Volume Synchronized Probability of Informed Trading (VPIN)
- Probability and Performance Measurement