/ Virtual

Analyzing Big Data with Microsoft R Server

In this blended learning experience of online and virtual instructor led course via edX, learn how to use Microsoft R Server to analyze large datasets using R, one of the most powerful programming languages.

REGISTER NOW
/ Virtual

Analyzing Big Data with Microsoft R Server

In this blended learning experience of online and virtual instructor led course via edX, learn how to use Microsoft R Server to analyze large datasets using R, one of the most powerful programming languages.

REGISTER NOW

About the course

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.

In this course, we will show you how to use MRS to run an analysis on a large dataset and provide some examples of how to deploy it on a Spark cluster or a SQL Server database. Upon completion, you will know how to use R for big-data problems.

Prerequisites

Since RevoScaleR is an R package, we assume that the course participants are familiar with R. A solid understanding of R data structures (vectors, matrices, lists, data frames, environments) is required. For example, students should be able to confidently tell the difference between a list and a data frame, or what each object is generally a good representation for and how to subset it. Students should be familiar with basic programming concepts such as control flows, loops, functions and scope. Students should have a good understanding of how to write and debug R functions. Finally, students are expected to have a good understanding of data manipulation and data processing in R (e.g. functions such as merge, transform, subset, cbind, rbind, lapply, apply). Familiarity with 3rd party packages such as dplyr is also helpful.
Courses:
• DAT204x: Introduction to R for Data Science : https://www.edx.org/course/introduction-r-data-science-microsoft-dat204x-2
• DAT209x: Programming in R for Data Science : https://www.edx.org/course/programming-r-data-science-microsoft-dat209x-1

Technologies covered

R Language
Course type
Self-paced
Course topics
R Language
Course level
Intermediate
Intended audience
Data scientists familiar with the R programming language
Instructors
Seth Mottaghinejad
Location
Virtual
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