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Introduction to Data Mining:
This course covers the two core paradigms that account for most business applications of data mining: classification and prediction. The course includes hands-on work with XLMiner, a data-mining add-in for Excel.
Instructor: Dr. Anthony Babinec Dates: March 4 - April 1, 2011
Forecasting:
This course will teach you how to choose an appropriate time series
model, fit the model, to conduct diagnostics, and use the model for
forecasting. The course will focus on Autoregressive (AR), Moving
Average (MA), combined ARMA, and Box Jenkins type models.
Instructor: Dr. Galit Shmueli Dates: Mar 25 - Apr 22, 2011
Programming in R:
The aim of the course is to give you the skills to work with a variety of data types and data sources in R. You'll also learn some techniques for programming "in-the-large", when you are trying to provide a suite of functions to flexibly solve a large class of problems.
Instructor: Dr. Hadley Wickham Dates: Apr 15 - May 13, 2011
Matrix Algebra Review:
This course will provide the basics of vector and matrix algebra and operations necessary to understand multivariate statistical methods, including the notions of the matrix inverse, generalized inverse and eigenvalues and eigenvectors. After successfully completing this course, you will be able to use and understand vector and matrix operations and equations, find and use a matrix inverse, and use and understand the eigenset of a symmetric matrix.
Instructor: Dr. Robert LaBudde Dates: Apr 15 - May 5, 2011
Interactive Data Visualization:
This course is about the interactive exploration of data, and how it is achieved using state-of-the-art data visualization software. Participants will learn to explore a range of different data types and structures. They will learn about various interactive techniques for manipulating and examining the data and producing effective visualizations.
Instructor: Dr. Galit Shmueli Dates: Apr 29 - May 27, 2011
Graphics in R:
The aim of this course is to teach you how to produce statistical plots of data using the R language and environment for statistical computing and graphics. The creation of standard plots such as
scatterplots, bar plots, histograms, and boxplots will be covered and time will be spent on the underlying model used to produce plots in R so that you can extensively customize these plots.
Instructor: Dr. Paul Murrell Dates: Apr 29 - May 27, 2011
DM Practicum - Statistica:
The purpose of this course is to take the knowledge learned in Introduction to Data Mining and Data Mining: Unsupervised Techniques and apply it using Statistica.
Instructor: Dr. Robert Nisbet Dates: Jun 10 - July 8, 2011