Certificate in Data Analysis with R

Practice Exam
Take Free Test

 

Data Analysis with R

 

About Data Analysis with R

R programming language is an open-source language used for statistical computation or graphics, and R analytics is data analytics utilizing R programming language. This programming language is frequently employed in data mining and statistical analysis. It may be applied to analytics to find trends and create useful models.

Why is Data Analysis with R important?

As R features static visuals that generate high-quality data visualizations, many data scientists utilize it for studying data. Additionally, the programming language contains a sizable library that offers interactive visuals and facilitates the analysis of data visualization and representation.

The ability of R to interact with NoSQL databases and analyze unstructured data is another crucial feature. In Data Science applications where a pool of data has to be evaluated, this is particularly helpful. R allows data scientists to use machine learning algorithms to learn about the future.

Who should take the Data Analysis with R Exam?

  •  
  • R Programmer
  • Data Analyst
  • Stategy Analyst
  • Statistical Programmer
  • Python Professionals

Data Analysis with R Certification Course Outline

 

  1. Exploratory data analysis (EDA)
  2. R Basics
  3. Explore One Variable
  4. Explore Two Variables
  5. Explore Many Variables
  6. Diamonds and Price Predictions

Certificate in Data Analysis with R FAQs

• Linear and nonlinear modelling

• Statistical tests

• Classification

• Time-series analysis

• Clustering

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. 

 

This exam is best suited for-

• Working Professionals

• Programmers

• Software developers

• Graduates

• Executive managers

 

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