The Revolution Analytics is one of the most talked-about and recent acquisitions of Microsoft. The company is dedicated for designing tools for working with big data problems using the statistical programming language R. Combining commercial tools with an open source model, Revolution Analytics is a hub supporting a variety of tools for both personal and academic use. Moreover, the software offers a separate set of great advantages related to managing massive amounts of data. After the acquisition, several major updates were made to the R tools to make the programming language more efficient and worth it for an improved data strategy. This led to higher demand for the R programming language for data strategy purpose.
Currently, the R platform is one of the most prominent aspects of Microsoft server products, with versions that support both Suse Linux and Red Hat Linux.
Why Microsoft R for Big Data Analysis
Organizations are now in need of actionable data, which can only be extracted from Big Data. And analytics is what can help with it. The open source R can also be utilized as the key tool used by data scientists to support big data analysis for enterprises. To gain the statistical expertise to get started with R, it is best if the organizations train teams on the programming language. The training can also help the team work out the Comprehensive R Archive Network. This is the application library for the R language, which offers more than 9000 statistical algorithms and modules to help with big data analysis.
With data science training for R, the team will be more familiar with the programming language's vision. The key is to cross the boundaries set between on-premise servers, desktop, and the cloud. Local data services that work with Spark or Hadoop are also supported by Microsoft R, while the Azure platform allows the programming language to work together with HDInsight services for better details.
Big Data Training for Microsoft R
Big data training enables data scientists to make the most out of R. It is, in fact, the most reliable tools that can be used for maximum big data benefits with data science training. This further simplifies the already relatively simple R language, to help you gain proper knowledge of statistical analytics for maximum gains. Training also helps wit understand the core concepts associated with the complex statistical functions and features of a programming language. It isn't really about the abilities of a team that could write the R code, it is more about the understanding of the results you can generate using the program and implementing it.
This is by far the most significant issues that an organization has to deal with while working with big data. Processing the information may be easier but producing the analysis you want and understanding the results is the whole new level. That's where you need the skills to interpret the results you get for improved business decision making. R certain can be very helpful with that, especially if your team is equipped with the skills and knowledge required for breaking up the result into actionable information. To make this simpler, use the built-in graphing tools and let the visualization key measures help you understand the statistics.
Working with Microsoft R
The open source Microsoft R can help the team get a grip on what R is all about before investing in the actual server products for better results. The Microsoft R server is a great tool for experimenting with innovative analytical lifecycle from scratch. The procedure includes data preparation, model development, and using the model into tools to help with business applications. Microsoft R is also the perfect example of high performance and multi-threading, although the features are mostly compatible with the basic functions. In fact, R is the most preferred tool for big data statistics, machine learning, and predictive modeling.
As far as utilizing the tool is concerned, it is time to acknowledge and appreciate that with R, more than a single type of analytics function or thing can be carried out:
- Data Preparation: R helps with elements such as time stamping and de-duplication of data to know when the data was created to stay up-to-date about that information.
- Data exploration: the steps involved in learning about the core characteristics of data sets.
- Data visualization: graphs and charts that make data interpretation easier.
- Data modeling: establishing the core knowledge to identify the different parts of data connected with each other.
While any other tool can help you establish the big data foundations, you need Microsoft R for ensuring multiplatform support from all directions. Since Microsoft is all about promoting the open-source religion, that's one interesting tool you need for your big data.
Microsoft R Open vs. CRAN
Now you get to deal with the big question: do you need Microsoft R CRAN or Open? Of course, it is a very subjective choice based on your specific requirements, but it is important to know that there isn't any functionality loss if you use R Open instead of CRAN. With the former, you may still be able to use the CRAN distribution functionality and even gain access to all development environments and libraries, including Jupyter or RStudio. Additionally, the benefits of using Microsoft R Open is that you can enjoy the flexibility of an open-source project without compromising on the reliability of a large corporation guarantee. You can work under great confidence and rely on the analyses for important decision-making.
If you need additional improvements in the performance with R, then indeed R Open is a preferable solution. It also helps you with the reproducibility and efficiency of your R scripts.
It wouldn't be wrong to state that Microsoft R is serving as the bridge between cloud data and on-premise data. And to gain the maximum out of Microsoft R for applied data science expertise, training the team is the best bet. If you are looking for professionals for a suitable training program, we are available at your service.