R Programming Data Analyst Learning Path, is a tour through the most important parts of R, the statistical programming language, from the very basics to complex modeling. It covers reading data, programming basics, visualization, data munging, regression, classification, clustering, modern machine learning, network analysis, web graphics, and techniques for dealing with large data, both in memory and in databases.
Reactive programming is shaping the future of how we model data. With reactive, not only can you concisely wrangle and analyze static data, you can effectively work with data as a real-time infinite feed. Reactive Extensions (Rx) first gained traction in 2009 and has been ported to over a dozen major languages and platforms.
Refine your data science skills with the heavy armory of tools provided by Julia
Unleash the powerful capabilities of R to work effectively with data.
Enhance your PHP 7 development skills
Learn and master the industry-standard language for data science
A comprehensive guide to working on statistical data with the R language.
Learn to perform efficient data analysis using Haskell