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Data Analyst Skills Test to identify high-potential candidates with excellent data analysis skills

The Data Analyst Skills Test forms the core of the pre-hire assessment process, which entails assessing candidates' data analysis skills objectively and efficiently. This online assessment will enable recruiters and employers to hire job-fit applicants for roles such as Big Data Analyst (using R Programming), Database Analyst, etc. The Data Analyst Test evaluates proficiency in database query writing, SQL server basics, R-programming and R-libraries.

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Inside this Data Analyst (using R) Assessment

The Data Analyst Test helps tech recruiters assess an individual's understanding of data analysis using R language. Designed and validated by subject matter experts (SMEs), this online evaluation enables recruiters and hiring managers to assess proficiencies in database query writing, hands-on programming, SQL server basics, R-programming, and R-libraries. Additionally, candidates are expected to solve coding problems in real-time, which are then evaluated for preciseness and quality. The 60-minutes test contains 18 MCQs and two coding problems. Recruiters can customize their tests based on the required skills and difficulty level. 

The following roles can be primarily evaluated using this Data Analyst Test: 

  • Big data analyst (using R Programming) 
  • Database analyst 

Overview

Skilled data analysts proficient in R programming are some of the most in-demand professionals globally. Nowadays, the pressing priority for hiring the best data analysts is observed in many companies and industries. Any business that excessively relies on data needs skilled analysts to analyze it. A data analyst generates insights using data so stakeholders can make business decisions and reach meaningful conclusions based on those insights. The analyst's role and KRAs will vary slightly across industries, organizations and departments.

Even for aspiring data analysts or researchers, the knowledge from importing data and cleaning it to manipulating and visualizing it in R is a must. R skills are invaluable for analysts in addition to understanding popular R packages like ggplot2, dplyr, tidyr, readr, etc. As they work with various crucial datasets, data analysts are known for their statistical skills for hypothesis testing, data manipulation, and exploratory data analysis (EDA) skills.

With data becoming the most important currency for businesses to operate at their maximum potential, hiring top data analyst talent has become the foremost concern for employers. 

In addition, hiring candidates for data analyst roles has become arduous because finding high-quality talent in a highly competitive job market is more challenging than it seems. Moreover, talent assessment is significantly difficult because the one-size-fits-all approach does not work here, and every organization's talent needs are specific to its business objectives.

An efficient way to determine if a candidate is a good fit for the role is to assess an applicant's employability skills using the Data Analyst Skills Test in the hiring process. Hiring managers will understand the extent to which the candidate's on-the-job skills and work readiness match the organization's requirements with this online screening method. As candidates undergo the vetting and screening through the Data Analysis Assessment, employers can rest assured that the selected candidates are up to the required standard in terms of skills, competencies, and experience. Add to this the fact that the assessment result reports thoroughly capture every aspect of a candidate's performance and give in-depth insights into their potential fit.

SKILL LIBRARY

This Data Analyst Skills Test is a part of following Skills Libraries

The data analyst skill assessment gauges expertise in performing data analysis and statistical operations in the R language. This online assessment is created by a team of SMEs and is ideal for assessing beginner to moderately experienced data analysts.

Data Analyst Test competency framework

Get a detailed look inside the test

Data Analyst competencies under scanner

Data Analyst

Competencies:

Database Query Writing

This data analyst test evaluates proficiencies in creating Database Query Questions in MS SQL language.

Hands on Programming

This data analyst assessment evaluates a programmer's efficiency in creating codes in R programming langauge.

SQL Server Basics

This data analyst test evaluates a candidate's skills in SQL Server - General on both intermediate and difficult level.

R Programming

This data analyst assessment test evaluates a candidate's applied skills in R - Functions.

R Libraries

This data analyst assessment test evaluates a candidate's applied skills in R - DPLYR and R - GGPLOT2 on both intermediate and difficult level.

Customize This Data Analyst Skills Test

Flexible customization options to suit your needs

Set difficulty level of test

Choose easy, medium or hard questions from our skill libraries to assess candidates of different experience levels.

Combine multiple skills into one test

Add multiple skills in a single test to create an effective assessment. Assess multiple skills together.

Add your own questions to the test

Add, edit or bulk upload your own coding questions, MCQ, whiteboarding questions & more.

Request a tailor-made test

Get a tailored assessment created with the help of our subject matter experts to ensure effective screening.

The Mercer | Mettl Data Analyst Assessment Test Advantage

The Mercer | Mettl Edge

  • Industry Leading 24/7 Support
  • State of the art examination platform
  • Inbuilt Cutting Edge AI-Driven Proctoring
  • Simulators designed by developers
  • Tests Tailored to Your business needs
  • Support for 20+ Languages in 80+ Countries Globally

Data Analyst Test Can Be Setup in 4 Steps

Step 1: Add test

Add this test your tests

Step 2: Share link

Share test link from your tests

Step 3: Test View

Candidate take the test

Step 4: Insightful Report

You get their tests report

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Frequently Asked Questions (FAQs)

You can follow these simple steps to hire data analysts:

 1) Write a well-thought-out job description

 A well-explained job description can strike a responsive chord with data analyst aspirants. A thorough data analyst JD includes a well-written summary of the role, a list of responsibilities, and the required skills. In addition, you can have keywords that job seekers use to search for these jobs, such as:

  • Business analyst 
  • Financial analyst
  • Data analyst

 2) Pre-employment testing to assess data analysis skills and filter out ineligible candidates

 A Data Analysis Test is a form of a psychometric test prepared by subject matter experts to understand and assess a candidate's expertise in analyzing high-level quantitative data. The test outcome reveals how well the candidate will perform in the actual job.

 3) Interviewing candidates for data analyst positions 

 Potential candidates for these positions will be optimistic in answering data analyst interview questions regarding:

  • Advanced SQL and Excel
  • ETL processes
  • Statistics
  • Project management
  • Interpersonal skills

Listed below are some must-have skills for data analysts: 

  • R or Python-statistical programming 
  • Data visualization 
  • Critical thinking 
  • SQL 
  • Machine learning 
  • Microsoft Excel 
  • Presentation skills 

The answer to this question varies based on the type of company and the level to which an enterprise has adopted data-driven decision-making practices. However, the responsibilities of an analyst usually include the following:

  • Designing and maintaining databases and data systems; this includes tasks such as solving various data-related issues and coding errors. 
  • Data mining from primary and secondary sources and compiling that data in a human and machine-readable format 
  • Employing statistical tools to explain data sets, putting particular emphasis on trends and patterns that offer opportunities for implementing diagnostic and predictive analytics 
  • Exhibiting the relevance of their responsibilities concerning global, national, and local trends that influence both their company and industry 
  • Creating reports for senior leaders to communicate trends, predictions, and patterns effectively based on relevant data. 
  • Teaming up with engineers, programmers, and executive leaders to find and discuss avenues for process modifications, system improvements, and data governance policies 
  • Providing adequate documentation that enables stakeholders to comprehend the steps of the analysis process 

The demand for data analysts is at an all-time high, as stated by the World Economic Forum, highlighting that data analytics is among the top ten careers in 2020 and beyond. 

Data analytics is undergoing continuous evolution; it all began with descriptive analytics. Now, we have reached the stage where analytics can determine future outcomes through predictive analytics. In addition, the advent of technologies such as AI, ML, IoT, and cloud computing has enabled analytics to perform even more demanding operations. With the continuously evolving technology comes far more exciting forms of analytics that can make the impossible possible.

After passing a Data Analyst Test, candidates often grapple with the issue of what questions to look forward to in the interview and how to answer them. Even though interviews are a bit unnerving, you must be well-prepared to keep nervousness at bay. Given below are some common interview questions:

  • Why did you choose this career? 
  • Which area of expertise would you prefer? 
  • What kind of analytics tool experience do you possess? 
  • What has been your most challenging analysis assignment to date? 
  • What are your strengths? 
  • Can you work under pressure? 
  • Why should we hire you? 
  • How would you estimate the number of tourists visiting Paris every June? 

There are four types of data analysis: 

  • Diagnostic 
  • Descriptive 
  • Prescriptive 
  • Predictive  

Data analytics is undergoing continuous evolution; it all began with descriptive analytics. Now, we have reached the stage where analytics can determine future outcomes through predictive analytics. In addition, the advent of technologies such as AI, ML, IoT, and cloud computing has enabled analytics to perform even more demanding operations. With the continuously evolving technology comes far more exciting forms of analytics that can make the impossible possible.

Most people often think about what separates a good data analyst from an inadequate one. Are there some specific attributes, qualities, and traits that can make someone a competent analyst? If so, is there a way to imbibe these personal skills and put these into practice? The answer to these questions is quite simple. Given below are some habits that one can master to achieve great success in their endeavor:

  • Choose a storytelling approach, but keep it simple 
  • Your attention to detail matters 
  • Have a good understanding of the business operations 
  • Utilize data creatively 
  • Be a team player 
  • Always show a willingness to learn new skills and tools 
  • Make every mistake count

Data analysts don't need to have exceptional coding skills, but they must be well-versed in data visualization software, analytics software, and data management processes. Data analysts should also have strong communication skills because they have to work with teams with different technical knowledge levels.

The Mercer | Mettl Data Analysis Assessment is tailored for recruitment. However, we can do benchmarking for custom sample sets as well. Please write to us about your request; we will gladly assist you.

We have previously customized online Data Analyst Assessment reports based on the client’s requirements. So please write to us with your request, and we will be glad to provide you with a solution.

This exam measures the candidates’ abilities to extract information by analyzing data and suggesting possible outcomes and their proficiency in using R. The Data Analysis Online Test is essentially used for pre-employment screening. However, 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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