SKILL TRACK

Big Data with R

R has great ways to handle working with big data including programming in parallel and interfacing with Spark. In this track, you'll learn how to write scalable and efficient R code and ways to visualize it too.

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R Language
20 hours
5 Courses

1

Writing Efficient R Code

Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.

4 hours Play preview

Colin Gillespie

Assoc Prof at Newcastle University, Consultant at Jumping Rivers
2

Visualizing Big Data with Trelliscope

Learn how to visualize big data in R using ggplot2 and trelliscopejs.

4 hours

Ryan Hafen

Author of TrelliscopeJS
3

Scalable Data Processing in R

Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.

4 hours Play preview

Michael Kane

Assistant Professor at Yale University
4

Introduction to Spark in R using sparklyr

Learn how to analyze huge datasets using Apache Spark and R using the sparklyr package.

4 hours Play preview

Richie Cotton

Head of Content Quality at DataCamp
5

Parallel Programming in R

This course covers in detail the tools available in R for parallel computing.

4 hours Play preview

Hana Sevcikova

Senior Research Scientist, University of Washington
Statement of accomplishment

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Colin Gillespie

Assoc Prof at Newcastle University, Consultant at Jumping Rivers

Ryan Hafen

Author of TrelliscopeJS

Michael Kane

Assistant Professor at Yale University

Simon Urbanek

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

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Big Data with R

Writing Efficient R Code

Visualizing Big Data with Trelliscope

Scalable Data Processing in R

Introduction to Spark in R using sparklyr

Parallel Programming in R

Statement of accomplishment

Ready To Learn

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Big Data with R

Writing Efficient R Code

Visualizing Big Data with Trelliscope

Scalable Data Processing in R

Introduction to Spark in R using sparklyr

Parallel Programming in R

Statement of accomplishment

Ready To Learn