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Scalable Data Processing in R

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$12

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

4 hours

Course Description

R programmers have to deal with problems when data sets exceed the RAM. All variables are defaulty stored in memory. Learn how to extract, analyze, and process data from the disk. Split-apply - combine will be used. Also, you'll learn how to create scalable codes with the bigmemory and iotools packages. Federal Housing Finance Agency data will be used in this course. This data set is publicly available and records all mortgages that were held or securitized in the period 2009 to 2015.

Course Overview

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Virtual Labs

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International Faculty

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Case Based Learning

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Post Course Interactions

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Case Studies,Hands-On Training,Instructor-Moderated Discussions

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Case Studies, Captstone Projects

Skills You Will Gain

Prerequisites/Requirements

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Writing Efficient R Code

What You Will Learn

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You’ll learn tools for processing, exploring, and analyzing data directly from disk

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You’ll also implement the split-apply-combine approach and learn how to write scalable code using the bigmemory and iotools packages

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In this course, you'll make use of the Federal Housing Finance Agency's data

Course Instructors

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Michael Kane

Assistant Professor at Yale University

Michael Kane is an Assistant Professor at Yale University. His research is in the area of scalable statistical/machine learning and applied probability.
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Simon Urbanek

Member of the R-Core; Lead Inventive Scientist at AT&T Labs Research

Simon Urbanek is a member of the R-Core and Lead Inventive Scientist at AT T Labs Research. His research is in the areas of R, statistical computing, visualization, and interactive graphics.

Course Reviews

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Course Cover

$12

$6

49% OFF

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