Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: R is one of the most popular languages in the data science, statistical and machine learning (ML) community. Hover, when it comes to scalable data analysis and ML using R, many data scientists are blocked or hindered by (a) its limitations of available functions to handle large datasets efficiently, and (b) knowledge about the appropriate computing environments to scale R scripts from desktop exploratory analysis to elastic and distributed cloud services. In this tutorial  will discuss solutions that demonstrate the use of distributed compute environments and end to end solutions for R.  will present the topics through presentations and worked-out examples with sample code. In addition,  will provide a public code repository that attendees will be able to access and adapt to their own practice.  believe this tutorial will be of strong interest to a large and growing community of data scientists and developers using R for data analysis and modeling.
['John-Mark Agosta', 'Debraj GuhaThakurta', 'Robert Horton', 'Mario Inchiosa', 'Srini Kumar', 'Mengyue Zhao']
I am writing a research paper on a idea Scalable Data Analytics Using R: Single Machines to Hadoop Spark Clusters
[{"from": "human", "value": "I am writing a research paper on a idea Scalable Data Analytics Using R: Single Machines to Hadoop Spark Clusters"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: R is one of the most popular languages in the data science, statistical and machine learning (ML) community. Hover, when it comes to scalable data analysis and ML using R, many data scientists are blocked or hindered by (a) its limitations of available functions to handle large datasets efficiently, and (b) knowledge about the appropriate computing environments to scale R scripts from desktop exploratory analysis to elastic and distributed cloud services. In this tutorial  will discuss solutions that demonstrate the use of distributed compute environments and end to end solutions for R.  will present the topics through presentations and worked-out examples with sample code. In addition,  will provide a public code repository that attendees will be able to access and adapt to their own practice.  believe this tutorial will be of strong interest to a large and growing community of data scientists and developers using R for data analysis and modeling."}]