Download Writing Efficient R Code - DataCamp
Files[FreeCoursesOnline.Me] [DataCamp] Writing Efficient R Code - [FCO] 01_The art of benchmarking
- 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 (7.0 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)
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Writing Efficient R Code. Turbo Charged Code: Parallel Programming. Some problems can be solved faster using multiple cores on your machine.
Writing Efficient R Code. Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming. Start Course For Free. This chapter shows you how to write R code that runs in parallel.
Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead. As a Data Scientist, the majority of your time should be spent gleaning actionable insights from data - not waiting for your code to finish running. Writing efficient Python code can help reduce runtime and save computational resources, ultimately freeing you up to do the things you love as a Data Scientist. In this course, you'll learn how to use Python's built-in data structures, functions, and modules to write cleaner, faster, and more efficient code.
New R Course: Writing Efficient R Code. DataCamp's newest R course will teach you the main techniques to speed up your data analysis in R by reducing computational time. Writing Efficient R code by Colin Gillespie. Hello R users, we've got a brand new course today: Writing Efficient R Code by Colin Gillespie. The beauty of R is that it is built for performing data analysis. Writing Efficient R Code features interactive exercises that combine high-quality video, in-browser coding, and gamification for an engaging learning experience that will make you a master in writing efficient, quick, R code! What you'll learn: Chapter 1: The Art of Benchmarking. Data Science Instructor at DataCamp. Assoc Prof at Newcastle University, Consultant at Jumping Rivers. Ready To Learn? Join 4,990,000 data science learners today! Start Learning for Free.
Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming. Writing Efficient R Code. Daha Fazla Bilgi Al. 27 Kasım 2017, 06:38 ·.
Like data scientists, data engineers write code. ETL design: writing efficient, resilient and evolvable ETL is key. I’m planning on expanding on this topic on an upcoming blog post. They’re highly analytical, and are interested in data visualization. Unlike data scientists - and inspired by our more mature parent, software engineering - data engineers build tools, infrastructure, frameworks, and services. Architectural projections: like any professional in any given field of expertise, the data engineer needs to have a high level understanding of most of the tools, platforms, libraries and other resources at its disposal.
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
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.
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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