Learning the “skills of data science” is easiest in R The popularity of R isn’t the only reason to learn R, however.
Ultimately, to really learn data science, you need to learn the “core” skill areas: data manipulation, data visualization, and machine learning.
In selecting a language, you need a language that has significant capabilities in each of these areas. You need tools for performing each of these tasks, as well as resources for learning them in the language you choose.
As I noted above, you need to focus much more on process and technique, not syntax.
You need to learn how to think about solving problems.
You need to learn how to find insight in data.
To do this, you’ll need to master the 3 core skill areas of data science: data manipulation, data visualization, and machine learning. Mastering these skill areas will be easier in R than almost any other language.
This story contains interviews with David Smith, chief community officer at Revolution Analytics; Casey Herron, data scientist at Revolution Analytics; Tess Nesbitt, director of analytics at DataSong; and Solomon Messing, data scientist at Facebook.
The new magick package is an ambitious effort to modernize and simplify high-quality image processing in R. It wraps the ImageMagick STL which is perhaps the most comprehensive open-source image processing library available today.
The ImageMagick library has an overwhelming amount of functionality. The current version of Magick exposes a decent chunk of it, but being a first release, documentation is still sparse. This post briefly introduces the most important concepts to get started.
Clustering methods are used to identify groups of similar objects in a multivariate data sets collected from fields such as marketing, bio-medical and geo-spatial. They are different types of clustering methods, including:
Partitioning methods, Hierarchical clustering, Fuzzy clustering, Density-based clustering, Model-based clustering,
In this post you will complete your first machine learning project using R.
In this step-by-step tutorial you will:
Download and install R and get the most useful package for machine learning in R. Load a dataset and understand it’s structure using statistical summaries and data visualization. Create 5 machine learning models, pick the best and build confidence that the accuracy is reliable.