Resources to Learn Statistical Programming
A general skill that would be a valuable (perhaps even required) asset in any scientific laboratory is to know how to analyze data using computational methods. While there exist a variety of software you can use to analyze data, two of the most common — and free! — programs are R and Python.
NO EXPERIENCE IS REQUIRED TO LEARN FROM ANY OF THE FOLLOWING RESOURCES.
R and RStudio
R is a programming language and free software environment for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.
Python is a general purpose language, used by data scientists and developers, which makes it easy to collaborate across your organization through its simple syntax. People choose to use Python so that they can communicate with other people. The other reason is rooted in academic research and statistical models.
Other courses to learn statistics
The goal of this class is to provide an introduction to the art of computational modeling in Cognitive Science using the Matlab programming language. Topics will include, how we model learning and simple decisions, how we connect computational models to observed experimental data (such as choices and neural activations) and how we can use modeling to design better experiments to test complex hypotheses.
The class will be very much a “hands on” class, every week students will create a different model, starting in class and finishing at home. At the end, students should be able build a computational model from scratch, fit a model to data and determine which of a set of models best fits the observed experimental data.