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R for Data Science Solutions

R for Data Science Solutions

Free Practice Test

FREE
  • TypeExam Format
  • No. of Questions10
  • AccessImmediate
  • Access DurationLife Long Access
  • Exam DeliveryOnline
  • Test ModesPractice

Practice Exam

$9.99
  • TypeExam Format
  • No. of Questions148
  • AccessImmediate
  • Access DurationLife Long Access
  • Exam DeliveryOnline
  • Test ModesPractice, Exam

Online Course

$11.99
  • Content TypeVideo
  • DeliveryOnline
  • AccessImmediate
  • Access DurationLife Long Access
  • No. of Videos114
  • No. of hours05+ hrs

R for Data Science Solutions

R in data science is used to deal with, store and break down data. It can be used for data analysis and statistical demonstrating. R is an environment for statistical analysis. R has various statistical and graphical capabilities.


Table of Contents

  • Functions in R
  • Data Extracting, Transforming, and Loading
  • Data Pre-Processing and Preparation
  • Data Manipulation
  • Visualizing Data with ggplot2
  • Making Interactive Reports
  • Simulation from Probability Distributions
  • Statistical Inference in R
  • Rule and Pattern Mining with R
  • Time Series Mining with R
  • Supervised Machine Learning
  • Unsupervised Machine Learning

R for Data Science Solutions FAQs

Assuming you're passionate with regards to the statistical estimation and data visualisation portions of data analysis, R could be ideal for you. If, then again, you're interested in turning into a data scientist and working with large data, artificial insight, and profound learning algorithms, Python would be the better fit.

R is a Procedural Programming Language that breaks down a task into a sequence of Stages, Processes, and Subroutines. This allows R to easily transform data into significant Statistics, Graphs, and foster Statistical Learning Models for predictions and inferences.

R is a great language for programming beginners to learn, and you needn't bother with any prior experience with code to get it. Nowadays, R is easier to learn than any time in recent memory thanks to the assortment of packages.

 

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