Scalable Data Processing in R

DataCamp

(3)

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

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

Virtual Labs

International Faculty

Case Based Learning

Post Course Interactions

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

Case Studies, Captstone Projects

Skills You Will Gain

Prerequisites/Requirements

Writing Efficient R Code

What You Will Learn

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

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

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

Course Content

Expand all sections

Module 1: Working with increasingly large data sets

Module 2: Processing and Analyzing Data with bigmemory

Module 3: Working with iotools

Module 4: Case Study: A Preliminary Analysis of the Housing Data

Course Instructors

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.

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.

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