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R Programming in Data Science

High Velocity Data

High-velocity data—such as the information that springs from Twitter and IoT devices—comes barreling in at a speed beyond normal comprehension, demanding high-performance from both hardware and software. While it might not initially appear up to the challenge, the R programming language can be revved up to operate with high-velocity data. Written close to the metal by sitting directly on top of the C programming language, R provides a rich set of data structures and concepts. This course drills down into efficient R programming, providing practical strategies that can help you work your mojo on high-velocity data. Instructor Mark Niemann-Ross begins by sharing a framework for understanding the different types of high-velocity data. He then covers how to use R to acquire high-velocity data, as well as how to leverage profiling tools and optimize R code for use with high-velocity data. He wraps up by exploring how to use R to present data, including how to use Shiny—an R package that allows you to build web apps straight from R—for interactive dashboards.

Contents:

  • Introduction
  • 1. Problems and Opportunities with High-Velocity Data
  • 2. Using R to Acquire High-Velocity Data
  • 3. Profiling Tools for R
  • 4. Optimizing R to Process High-Velocity Data
  • 5. Using R to Present High-Velocity Data
  • Conclusion

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Rating Not enough ratings
Length 1h 21m
Starts On Demand (Start anytime)
Cost $29/month (Access to entire library- free trial available)
From LinkedIn Learning
Instructor Mark Niemann-Ross
Download Videos Only via the LinkedIn Learning mobile app
Language English
Subjects IT & Networking Data Science Programming
Tags IT Big Data R RStudio

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LinkedIn Learning

Rating Not enough ratings
Length 1h 21m
Starts On Demand (Start anytime)
Cost $29/month (Access to entire library- free trial available)
From LinkedIn Learning
Instructor Mark Niemann-Ross
Download Videos Only via the LinkedIn Learning mobile app
Language English
Subjects IT & Networking Data Science Programming
Tags IT Big Data R RStudio

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