In addition to the 2-day conference, USAR2009 offers the latest hands-on technical training in the industry.

Post-Conference Workshop

Data Mining Techniques in R|R-PLUS: Theory and Practice
Advanced R|R-PLUS programming

Conference plus 2 days of training
Attend all conference sessions, exhibit hall admission, meals, networking events, plus 2 days of R|R-PLUS training classes on April 29-30*

Conference plus 1 day of training
Attend all conference sessions, exhibit hall admission, meals, networking events, plus 1 days of R|R-PLUS training classes offered April 29.*

Note: No food or drink is allowed in the computer lab. Badges must be worn at all times during class and labs.

Post-Conference Training

Introduction to R|R-PLUS
This is a beginners couthat will introduce you to the basics of the software syntax. We will concentrate on learning the data structures and commands necessary for data analysis. This course is designed for those who want to learn to write programs to accomplish typical data-processing tasks, including creating graphics. The course will give beginners a strong foundation for becoming a versatile programmer.
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Introduction to R/S+ programming:Microarrays Analysis and Bioconductor.
This two-day beginner to intermediate course is designed for those who want to learn to write R/Splus programs to solve data analysis problems, especially microarrays. The second day of this class focuses on microarrays analysis with R/Splus and Biocondutor
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Data Mining Techniques in R|R-PLUS:Theory and Practice
This course gives students an understanding of R/Splus tools used to investigate the main tasks that predictive analytics and exploratory data mining is usually called upon to accomplish and data preparation which is universally held as the key to successful data mining. We focus on the most common data mining tasks which are: Description, Estimation, Prediction, Classification, Clustering, Association and the need for Dimension Reduction with Principal Components and Factor Analysis. Analytical methods used in the class include decision trees, logistic regression, neural networks, link analysis (social networks) and Kernel-based Methods (SVMs).
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R|R-PLUS Systems: Advanced programming
This advanced course is designed for people who use R or S-Plus in their day-to-day work and want to maximize the efficiency of their programs. Participants will learn in depth advanced programming techniques that are available in R and S-Plus. This course will improve your general strategies and extend your programming skills.
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Advanced Statistical Modelling with R|S-PLUS $.
(This workshop is not included in the conference training packages).
R and S-PLUS offer a large choice of facilities for classical and modern approaches to statistical modelling. R is presented as a complete data analysis and graphics environment and will focus on R programming strategies for handling standard and non-standard statistical modelling problems.
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Statistics II: Regression Modeling Strategies in R/Splus. is an additional $.
This two-day course is designed for persons interested in multivariable regression analysis of univariate responses, in developing, validating, and graphically describing multivariable predictive models. The first part of the course presents the following elements of multivariable predictive modeling for a single response variable: using regression splines to relax linearity assumptions, perils of variable selection and overfitting, where to spend degrees of freedom, shrinkage, imputation of missing data, data reduction, and interaction surfaces. Then a default overall modeling strategy will be described. This is followed by methods for graphically understanding models (e.g., using nomograms) and using re-sampling to estimate a model? likely performance on new data. Then the freely available R and S-Plus Design package will be overviewed. Design facilitates most of the steps of the modeling process. Next, statistical methods related to binary logistic models will be covered. Three of the following case studies will be presented: an exploration of voting tendencies over U.S. counties in the 1992 presidential election, an interactive exploration of the survival status of Titanic passengers, an interactive case study in developing a survival time model, and a case study in Cox regression. In the hands-on computer lab students will develop, validate, and graphically describe multivariable regression models themselves. This short course will also survey the advantages of modeling in randomized trials. The methods covered in this course will apply to almost any regression model, including ordinary least squares, logistic regression models, and survival models.
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