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.
Learn to perform efficient data analysis using Haskell
Practice and apply R programming concepts for effective statistical and data analysis
The R programming language has arguably become the single most important tool for computational statistics, visualization, and data science. With this Learning Path, master the basics that you'll need as a data scientist. You'll work your data like never before.
With a gentle learning curve, Python is readable, writeable, and endlessly powerful. Its simplicity lets you become productive quickly. This Learning Path provides a solid introduction to Python, and then teaches you about algorithms, data modeling, data structures, and other tools that make Python the ideal choice for working with data.