Modern scientific research takes advantage of programs such as Python and R that are open source. As such, they can be modified and shared by the wider community. Additionally, there is added functionality through additional programs and packages, such as IPython, Sweave, and Shiny. These packages can be used to not only execute data analyses, but also to present data and results consistently across platforms (e.g., blogs, websites, repositories and traditional publishing venues).
The goal of the course is to show how to implement analyses and share them using IPython for Python, Sweave and knitr for RStudio to create documents that are shareable and analyses that are reproducible.
Course outline is as follows:
1) Use of IPython notebooks to demonstrate and explain code, visualize data, and display analysis results
2) Applications of Python modules such as SymPy, NumPy, pandas, and SciPy
3) Use of Sweave to demonstrate and explain code, visualize data, display analysis results, and create documents and presentations
4) Integration and execution of IPython and R code and analyses using the IPython notebook