Analyzing Big Data with Microsoft R Server
Learn how to use Microsoft R Server (MRS) to analyze large datasets using R, one of the most powerful programming languages.
This course is currently only available for on-site client instruction.
If you are interested in training, please contact us at [email protected]
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 as a programming language are its succinctness and its 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 it is memory-bound. In other words, R needs to load the data in its entirety into memory (like any other object). This is one of the reasons R has been more reluctantly received in industry, where data sizes are usually considerably larger than in academia.
This course covers the following
- Deploying and scaling. We talk about RevoScaleR's write-once-deploy-anywhere philosophy and talk about what we mean by a compute context. We then take this into practice by deploying our code into SQL Server and Spark and talk about architectural differences.
Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. He has publications and patents in various fields such as microfluidics, materials science, and data science technologies. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming the ability to analyze data, as well as present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Data Inc. and provides in-person data science and python training courses to a variety of companies all over the world, including top banks such as Credit Suisse. Feel free to contact him on LinkedIn for more information on in-person training sessions.