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Working... ► Play all. Why R is Hard to Learn. By Bob Muenchen R has a reputation of being hard to learn.
Some of that is due to the fact that it is radically different from other analytics software. Some is an unavoidable byproduct of its extreme power and flexibility. And, as with any software, some is due to design decisions that, in hindsight, could have been better. If you have experience with other analytics tools, you may at first find R very alien. Quick-R: Home Page. An R Introduction to Statistics. Swirl: Learn R, in R. Code School - Try R. Beginner's guide to R: Introduction.
R is hot.
Whether measured by more than 4,400 add-on packages, the 18,000+ members of LinkedIn's R group or the close to 80 R Meetup groups currently in existence, there can be little doubt that interest in the R statistics language, especially for data analysis, is soaring. Why R? It's free, open source, powerful and highly extensible. "You have a lot of prepackaged stuff that's already available, so you're standing on the shoulders of giants," Google's chief economist told The New York Times back in 2009. R Tutorial — R Tutorial. DataVisualization. Statistical Learning. About This Course This is an introductory-level course in supervised learning, with a focus on regression and classification methods.
The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics.
We focus on what we consider to be the important elements of modern data analysis. Coursera - Free R Progamming Course.