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Scalable Data Processing in R

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5

(3)

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Course Report - Scalable Data Processing in R

Course Report

Find detailed report of this course which helps you make an informed decision on its relevance to your learning needs. Find out the course's popularity among Careervira users and the job roles that would find the course relevant for their upskilling here. You can also find how this course compares against similar courses and much more in the course report.

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.

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Highlights

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Pedagogy

Top 10 Percentile

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Rating & Reviews

Top 30 Percentile

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Hands on training

Top 1 Percentile

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Parameters

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Pedagogy

Acquire all major R Programming skills in this course for seamless integration into your daily life. Develop a versatile skill set, allowing you to confidently apply what you've learned in various practical scenarios, enhancing your daily experiences and overall proficiency. An exceptional course in R Programming, this stands out for its Self Paced learning approach. Learners have the flexibility to progress at their own speed, tailoring the experience to their individual needs.

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Hands on training

This course stands out as one of the top 1 percentile options in R Programming, offering unparalleled hands-on training. Learners gain practical experience and skills through immersive learning, preparing them for real-world challenges. This course is a standout choice among top R Programming courses, offering comprehensive hands-on training, a capstone project, case-based learning, and virtual labs. Learners gain practical experience through interactive training and apply their skills in real-world scenarios. This holistic approach equips learners with both theoretical knowledge and practical skills for success in the R Programming industry.

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Rating & Reviews

This highly acclaimed course is among the top-rated in R Programming, boasting a rating greater than 4 and an overall rating of 5.0. Its exceptional quality sets it apart, making it an excellent choice for individuals seeking top-notch learning experience in R Programming.

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

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 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

Average Rating Based on 3 reviews

5.0

100%

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