Data Mining in R online course taught by Luis Torgo at statistics.com

An interested PR piece I got from Janet Dobbins: ————— Luis Torgo is teaching an online course, “Data Mining in R: Learning with Case Studies” at statistics.com. The course starts June 17 – July 15. Brief Description: The main goal of this course is to teach users how to perform data mining tasks using R. Instructor(s): Dr. Luis Torgo Level: Intermediate Who Should Take This Course: R users who want to learn how to apply R to data mining. Data mining analysts in search of new tools. Students in statistics.com’s PASS program in Data Mining seeking an affordable data mining tool. Note that working in R will be more involved than using a specially designed interface for data mining, such as those found in major commercial data mining programs. Dates: June 17, 2011 to July 15, 2011 Here’s a little bit about statistics.com: Statistics.com began offering online courses in statistics in 2002, and now offers approximately 100 courses each year (12 of which are on using R.) Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics, and courses in biostatistics, engineering statistics, data mining, business analytics, survey statistics, and environmental statistics. Statistics.com has over 60 instructors, who are recruited based on their expertise in various areas in statistics (most are the authors of well-regarded texts in their area). An advisory board of senior faculty who has made important contributions to the field of statistics or online education in statistics (each with over five years of teaching experience online at Statistics.com) advises the president on curriculum and standards. Courses are scheduled for specific calendar periods (3 or 4 weeks), but do not require students to be online at particular times. Students discuss statistical questions with the instructor via a private class discussion forum, teaching assistants offer feedback on homework assignments (most of which involve the use of statistical software), and administrative staff handle student services and inquiries by email or phone. Thanks, Janet Dobbins