title	author	date	output	header-includes
Stat 5014	Bob Settlage	August 2017	pdf_document fig_caption fig_height fig_width html_document classoption true 6 7 default landscape	\setlength\parindent{24pt} \usepackage{MnSymbol} \usepackage{mathrsfs}

This course is an introduction to computing for statistics. Recently, open source platforms have proliferated and are becoming the defacto standard for Data Scientists. R and Python are arguably the two most important languages in a Data Scientists toolbox. In this class, we introduce both languages with a focus on R. We touch on notebook style analysis for enabling and performing Reproducibile Research. Throughout the course, we will use \LaTeX via Markdown for type setting. In many collaborative environments, version control and collaborative development is a crucial technology and concept. Here, we will use Git as the backbone of the course for homework submission, collaborative learning via code sharing, and crowd sourced design. Finally, we end the class with methods for large (TB) datasets and discuss how these methods could be used to handle other large (long) computes.

Course learning objectives:

• Good programming practices
• Reproducible research concepts
• Data cleaning and munging
• R programming
• Git fundamentals
• Markdown
• Python basics