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Data Analysis with R

Data Analysis with R

Free Practice Test

FREE
  • TypeExam Format
  • No. of Questions20
  • AccessImmediate
  • Access DurationLife Long Access
  • Exam DeliveryOnline
  • Test ModesPractice

Practice Exam

$7.99
  • TypeExam Format
  • No. of Questions251
  • AccessImmediate
  • Access DurationLife Long Access
  • Exam DeliveryOnline
  • Test ModesPractice, Exam

Online Course

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  • Content TypeVideo
  • DeliveryOnline
  • AccessImmediate
  • Access DurationLife Long Access
  • No of videos-
  • No of hours-
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Data Analysis with R


Data analysis is the upcoming source of numerous opportunities. It is focused on analysing data sets and then applying statistical techniques for making decisions and predicting the success of businesses. R software is widely used for data analysis and graphical representation of data. Thus, getting certified in Data analysis and R will create huge opportunities for career progression for you.


Course Outline

The topics covered in this exam are as follows-

Introduction

Descriptive Statistical Measures

Probability Distributions

Sampling and Estimation

Statistical Inference

R Programming Language Introduction

Reading Data from files

Probability Distributions

Statistical Models in R

R Graphics Facilities

R Data Import/Export

Data Analysis with R FAQs

Data analysis is the upcoming source of numerous opportunities. It is focused on analysing data sets and then applying statistical techniques for making decisions and predicting the success of businesses. R software is widely used for data analysis and graphical representation of data. 


The topics covered in thisexamare as follows-

Introduction

Descriptive Statistical Measures

Probability Distributions

Sampling and Estimation

Statistical Inference

R Programming Language Introduction

Reading Data from files

Probability Distributions

Statistical Models in R

R Graphics Facilities

R Data Import/Export

This exam is best suited for-

Working Professionals

Programmers

Software developers

Graduates

Executive managers


Linear and nonlinear modelling

Statistical tests

Classification

Time-series analysis

Clustering

 

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