# R

Since its release in 1997, R has emerged as a popular tool for statistical analysis and research. The flexibility and extensibility of R are keys attributes that have driven its adoption. Some of the advantages of R are related to the command line interface (CLI) form in which it is used. However, this does add to the challenge of learning to use R. The goal of this course is to build familiarity with the basic R toolkit for statistical analysis and graphics. Specifically:

- Become comfortable using R to manage and manipulate data
- Become familiar with some of R's most commonly used statistical procedures
- Explore simple programming in R
- Develop good analytical practices including documenting analysis and data manipulation, and collaborating with others in the R user/learner community

Since its release in 1997, R has emerged as a popular tool for statistical analysis and research. The flexibility and extensibility of R are keys attributes that have driven its adoption. Some of the advantages of R are related to the command line interface (CLI) form in which it is used. However, this does add to the challenge of learning to use R. The goal of this course is to build upon the knowledge and experience gained in STAT 484. Specifically:

- Become familiar with using R for common statistical analyses
- Learn how to use R graphics to develop sophisticated figures
- Explore simple programming in R
- Develop good analytical practices including documenting analysis and data manipulation, and collaborating with others in the R user/learner community

Data mining and statistical learning methods use a variety of computational tools for understanding large, complex datasets. In some cases, the focus is on building models to predict a quantitative or qualitative output based on a collection of inputs. In others, the goal is simply to find relationships and structure from data with no specific output variable. This course takes an applied approach to understand the methodology, motivation, assumptions, strengths, and weaknesses of the most widely applicable methods in this field.

Time series data are intriguing yet complicated information to work with. While this course will provide students with a basic understanding of the nature and basic processes used to analyze such data, you will quickly realize that this is a small first step in being able to confidently understand what trends might exist within a set of data and the complexities of being able to use this information to make predictions or forecasts. Yet, whether it is financial, medical or weather related, this type of data is quite frequently found in much of our daily lives.