Data Analyst in R
Data analysis is widely in use these days, and the way to perform data analytics using R is also becoming a subject of utmost importance. The reason being is that R is an essential tool for data analysts, which enables them to perform data analysis and visualization. A vital approach employed in data analytics using R is called exploratory data analysis, which includes summarization and visualization of data sets. The concept, introduced by John Tukey, focuses on analyzing data’s basic structures and variables to gain a better understanding of the data set, to get a solid understanding of how the data originated and what statistical analysis approach would be suitable for data analysis.
What is R?
R is a programming language and environment adapted by statistical experts for data analysis and developing statistical software. Even though there are different data analysis software like Python, analyzing data using R is a different ball game altogether. A data analyst evaluating data using R would give written commands to the software to go about it. However, the best thing about using R is that it enables the analysts to collect large data sets and add multiple commands together and synchronously optimize all the commands together. What makes R indispensable in data analysis is its ability to process a large number of commands collectively. It autosaves all the data and work progress, and allows analysts to easily correct minor mistakes so that they do not have a hard time shuffling through different commands to backtrace their steps, and eventually find and fix the error. R is available for download for these platforms: Windows, Linux, Unix, or OS X.
What is a data analyst test?
Data analysts perform quantitative testing that converts data into applicable insights and offers data-driven analytical support for critical projects. A data analyst who is well versed in R should be a data visualization expert and be able to pull out and utilize that data. The data analyst assessment test helps you in evaluating:
Are you looking for a customised version of this test. Or looking to get a new test build according to your requirements from scratch? Reach out to our subject matter experts and discuss the same.
Why should you use Mercer| Mettl’s data analyst assessment?
Mettl's data analyst (with proficiency in R) assessment is a well-thought-out offering catering to the level of skills that a beginner to moderately experienced data analyst would be supposed to possess. The method of gauging candidates’ expertise through Mettl assessments is your best bet for securing the best professionals with exceptional analytical and data interpretation skills for the role of the data analyst.
Key profiles the test is useful for:
|Number of Sections||5|
|Number of Questions||20|
|Test Duration||60 Minutes|
|Number of MCA/MCQ questions based on SQL Server Basics||5|
|Number of MCA/MCQ questions based on R||4|
|Number of MCA/MCQ questions based on R Packages||9|
|Number of database query writing questions that intend to assess the query writing skills in MS SQL Server||1|
|Number of hands-on programming questions that intend to evaluate the data analyzing skills in R Programming||1|
Candidates with a work experience of 1-3 years are eligible to appear for the test
Difficulty level: Moderate
Q. Can candidates be benchmarked based on the internal sample set?
A. Yes, at Mettl, it can be done. Should you need any further information, please write to us, and we will be happy to assist you.
Q. Is it possible to customize the report as per the need?
A. Yes, we can do it. We have already done it in the past for our clients. Should you need any further information, please write to us, and we will be happy to find the best solution for you.
Q. What is a data analyst assessment?
A. This exam measures the candidates’ abilities to extract information by analyzing data and suggesting possible outcomes, as well as their proficiency in using R. This test is essentially used for pre-employment screening. Data analysts who use R for their daily work should know how to leverage the functionality given by R Packages to unveil powerful insights from data.
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