The open-source programming language R has for a long time been popular (particularly in academia) for data processing and statistical analysis. Among R's strengths are that it's a succinct programming language and has an extensive repository of third party libraries for performing all kinds of analyses.
Together, these two features make it possible for a data scientist to very quickly go from raw data to summaries, charts, and even full-blown reports. However, one deficiency with R is that traditionally it uses a lot of memory, both because it needs to load a copy of the data in its entirety as a data.frame object, and also because processing the data often involves making further copies (sometimes referred to as copy-on-modify). This is one of the reasons R has been more reluctantly received by industry compared to academia.
The main component of Microsoft R Server (MRS) is the RevoScaleR package, which is an R library that offers a set of functionalities for processing large datasets without having to load them all at once in the memory. RevoScaleR offers a rich set of distributed statistical and machine learning algorithms, which get added to over time.
Finally, RevoScaleR also offers a mechanism by which we can take code that we developed on our laptop and deploy it on a remote server such as SQL Server or Spark (where the infrastructure is very different under the hood), with minimal effort.
This Analyzing Big Data with Microsoft Certificate from EdX in partnership with MicrosoftX is part of the Microsoft Professional Program Certificate in Big Data, and the Microsoft Professional Program Certificate in Data Science.
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