Upcoming R courses from Statistics.com

October 26, 2010

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

The online training provider Statistics.com has three great courses based on R coming up in the next few months:

Nov. 5 – Dec. 3: "Graphics in R," with Paul Murrell
Nov. 20 – Dec. 18:  Support Vector Machines in R" with Dr. Lutz Hamel
Dec. 17 – Jan. 22: "Geostatistics in R" with Prof. David Unwin

The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and requires about 15 hours/week. You participate at your own convenience; there are no set times when you are required to be online.

More details about these courses appears after the jump, or click the links above to register.

Nov. 5 – Dec. 3: "Graphics in R," with Paul Murrell

“Graphics in R,” teaches you how to produce publication-quality statistical plots of data using R. It will cover plots such as scatterplots, bar plots, histograms, boxplots and Trellis plots.  It will review the underlying model used to produce plots in R so that you can extensively customize these plots.  Finally, the course will introduce the grid graphics system and look at producing unique plots from the ground up using basic components.

Dr. Paul Murrell, instructor for this course, is a Senior Lecturer in the Department of Statistics at the University of Auckland, New Zealand. Paul has been a member of the core development team for R since 1999, with a focus on the graphics system in R. He is the past Chair of the Section for Statistical Graphics of the American Statistical Association.  He has recently served as Editor-in-Chief of “R News”, the newsletter of the R project, and is an Associate Editor for “Computational Statistics” and “The Journal of Statistical Software”.  Participants can ask questions and exchange comments with Dr. Murrell via a private discussion board throughout the period.

Details and registration: http://www.statistics.com/ourcourses/graphicsR/


Nov. 20 – Dec. 18:  Support Vector Machines in R" with Dr. Lutz Hamel

Support vector machines (SVMs) have established themselves as one of the preeminent machine learning models for classification and regression over the past decade or so, frequently outperforming artificial neural networks in task such as text mining and bioinformatics.  Dr. Lutz Hamel, author of "Knowledge Discovery with Support Vector Machines" from Wiley will present his online course “Introduction to Support Vector Machines In R” Nov. 20 – Dec. 18 at statistics.com.

“Support Vector Machines in R” will give you an understanding on what is going on "under the hood" when using SVMs. After completing this course, you will be able to interpret the performance of SVM models and make appropriate choices for model parameters during the model evaluation and selection cycle. You will understand the difference between linear, polynomial, and gaussian kernels and know how to tune their parameters. In addition, you will have a deep understanding on how the cost constant "C" affects the quality of your models.

Dr. Lutz Hamel teaches at the University of Rhode Island and was the founder of the machine learning and data mining group there. He is the author of Knowledge Discovery with Support Vector Machines
(the course text). Before becoming an academic, Dr. Hamel was Director of Software Development at Thinking Machine Corporation, and Vice President of R&D for Bluestreak, where he oversaw the development of advanced technologies for online ad delivery and optimization, and directed the building of a next generation data warehouse-driven system for campaign analysis and design tools.

Participants can ask questions and exchange comments with Dr. Hamel via a private discussion board throughout the course.

Details and registration: http://www.statistics.com/ourcourses/SVM/


Dec. 17 – Jan. 22: "Geostatistics in R" with Prof. David Unwin

This course will teach users how to implement spatial statistical analysis procedures using R software. Topics covered include point pattern analysis, identifying clusters, measures of spatial association, geographically weighted regression and surface procession.

Dr. David Unwin is Emeritus Chair of Geography at Birkbeck College, University of London, and also a Visiting Professor in the Department of Geomatic Engineering at University College, also in the University of London. His work using and developing spatial statistics in research stretches back some 40 years, and he has authored over a hundred academic papers in the field, together with a series of texts, of which the most recent are his Geographic Information Analysis, 2nd edition (with D. O'Sullivan, 2010) and a series of edited collections at the interface between geography and computer science in "Visualization in GIS" (Hearnshaw and Unwin, 1994), "Spatial Analytical Perspectives on GIS" (Fischer, Scholten and Unwin, 1996), Virtual Reality in Geography (Fisher and Unwin, 2002) and, most recently representation issues in "Re-presenting GIS" (Fisher and Unwin, 2005). Having developed the world's first wholly internet-delivered Master's program in GIS in 1998, David Unwin has considerable experience of teaching and tutoring online.

Details and registration: http://www.statistics.com/ourcourses/GeostatsinR/

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