[DataCamp] Writing Efficient R Code - [FCO]

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[DataCamp] Writing Efficient R Code - [FCO] (Size: 102.9 MB)
  01_Welcome.mp4 7.4 MB
  02_Benchmarking.mp4 12.3 MB
  03_How goood is your machine.mp4 7.6 MB
  04_Memory allocation.mp4 8.4 MB
  05_Importance of vectorizing your code.mp4 5.9 MB
  06_Data frames and matrices.mp4 7.1 MB
  07_What is code profiling.mp4 11.4 MB
  08_Profvis larger example.mp4 7.6 MB
  09_Monopoly overview.mp4 4.3 MB
  10_CPUs why do we have more than one.mp4 3.3 MB
  11_What sort of programmings benefit from parallel computing.mp4 7 MB
  12_The parallel package parApply.mp4 7.4 MB
  13_The parallel package parSapply.mp4 11.8 MB
  14_You can write efficient R code.mp4 1.1 MB
  Discuss.FreeTutorials.Us.html 165.7 KB
  FreeCoursesOnline.Me.html 108.3 KB
  FreeTutorials.Eu.html 102.2 KB
  How you can help Team-FTU.txt 307.2 B
  Torrent Downloaded From GloDls.to.txt 102.4 B
  [TGx]Downloaded from torrentgalaxy.org.txt 512 B
  ▲ 20 total files

Description


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

Author : Colin Gillespie
Duration : 4 hours
Lectures : 14 Videos
Torrent Contains : 20 Files, 4 Folders
Course Source : https://www.datacamp.com/courses/writing-efficient-r-code

Course Description

The beauty of R is that it is built for performing data analysis. The downside is that sometimes R can be slow, thereby obstructing our analysis. For this reason, it is essential to become familiar with the main techniques for speeding up your analysis, so you can reduce computational time and get insights as quickly as possible.

About Author

Colin Gillespie - Assoc Prof at Newcastle University, Consultant at Jumping Rivers

Colin is the author of Efficient R Programming, published by O'Reilly media. He is an Associate Professor of Statistics at Newcastle University, UK and regularly works with Jumping Rivers to provide data science training and consultancy. He is the only person in history to move to Newcastle for better weather.

Table Of Content

• The Art of Benchmarking
• Fine Tuning: Efficient Base R
• Diagnosing Problems: Code Profiling
• Turbo Charged Code: Parallel Programming.

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