Data Analysis with R Practice Exam

Data Analysis with R Practice Exam

4.6 (1,001 ratings)
1,143 Learners

What’s Included

No. of Questions 340
Access Immediate
Access Duration Life Long Access
Exam Delivery Online
Test Modes Practice, Exam

 

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.

Data Analysis with R has gained immense popularity across the globe resulting in huge demand for certified professionals.

 

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.

Data Analysis with R certified professionals, executives and managers are in high demand in companies across the globe.

 

Who should take the Data Analysis with R Exam?

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

 

Knowledge and Skills required for the Data Analysis with R

Critical thinking and communication skills helps candidate to gain quick success for career in data analysis with R.

 

Data Analysis with R Practice Exam Objectives

Data Analysis with R exam focuses on assessing your skills and knowledge in data analysis and R programming language.

 

Data Analysis with R Practice Exam Pre-requisite

There are no prerequisites for the Data Analysis with R exam. Candidates who are well versed  in data analysis and R programming language can easily clear the exam.


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

 

Exam Format and Information

Certification name – Certificate in Data Analysis with R
Exam duration – 60 minutes
Exam type - Multiple Choice Questions
Eligibility / pre-requisite - None
Exam language - English
Exam format - Online
Passing score - 25
Exam Fees  - INR 1199


 

What We Offer?

Full-Length Mock Tests that include unique, exam-style questions to help you practice under real conditions.
Section-Wise Practice Questions for reviewing topic-based questions and instantly see where you stand in every section.
Detailed answers with a clear and thorough explanation to help you understand the concept, not just memorize answers.
Get a complete breakdown of your strengths, weaknesses, and progress after every attempt.
All question sets reflect the latest exam syllabus and format.
Unlimited Access to Practice anytime, as often as you want - no time limits or hidden restrictions.

100% Pass Guarantee

We have built the Practice Exams with a 100% unconditional Test Pass Guarantee! If you are unable to clear the exam, you can request a full refund guaranteed.

Reviews

How learners rated this courses

4.6

(Based on 1001 reviews)

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Kevin Brew

I appreciated the focus on RMarkdown and reproducible research. It helped me practice clean coding habits and understand how to handle missing data using imputation techniques.

Swiss Any

Perfect for mastering the Tidyverse and ggplot2. The questions on data wrangling with dplyr and reshaping with tidyr were high quality and very practical for real-world datasets.

Laura Zingy

A great review of statistical modeling and hypothesis testing in R. I found the questions on linear regression and p-values to be well-explained and technically thorough.

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