Data Science Aptitude Test

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15645+

Organizations Served

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Data science aptitude test helps recruiters evaluate candidates required technical as well as aptitude skills. This assessment is a good combination of questions on data science, SQL, data wrangling and quantitative aptitude to quantify candidates' aptitude for data science.  

About Data Science Aptitude Test

Data Science is an interdisciplinary field that incorporates data analysis fields including statistics, machine learning, and predictive analysis. Its purpose is to evaluate analytics and data science techniques. Methods and theories from other sectors, such as mathematics, information science, computer science, and statistics, are also used in data science.  

Data science aptitude test is designed and validated by experienced Subject Matter Experts (SMEs) to assess and hire data science professionals as per industry standards.   

Are you a jobseeker looking to sharpen your skills?

Test Summary

Data science aptitude test helps to screen candidates possessing the following traits: 

  • Good knowledge of Classification and Regression Algorithms 

  • Relevant experience in Data Extraction and Mining 

  • Strong knowledge of vImbalanced Datasets & Sampling Distributions 

  • Good experience in Optimizing Functions and Information Theory 

  • Hands-on experience & knowledge in Data Wrangling and Correction 

  • In-depth understanding of Numerical Ability 

This test is designed considering EEOC guidelines. It will help you assess & hire diverse talent without any bias. Recruiters/hiring managers can access comprehensive reports for each candidate that will give an overview of candidate’s performance in each section at a glance. Moreover, Test Insights section helps to identify job fit candidates more accurately with score distribution and  section analysis features.   

This test may contain MCQ's (Multiple Choice Questions), MAQ's (Multiple Answer Questions), Fill in the Blanks, Descriptive, Whiteboard Questions, Audio / Video Questions, AI-LogicBox (AI-based Pseudo-Coding Platform), Coding Simulations, True or False Questions, etc.     

Test Duration: 60 minutes

No. of Questions: 23

Level of Expertise: Entry Level/Mid/Expert

Useful for hiring

  • Data Science  Expert
  • Data Science  Engineer
  • Data Science Consultant

Topics Covered


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Classification and Regression Algorithms

This test helps recruiters to understand the candidate’s knowledge of dividing the dataset into different classes and ability to find the best fit line, which can predict the output more accurately.

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Data Extraction and Mining

This test helps recruiters in evaluating the skills of a candidate to analyze large data sets using machine learning, statistical and mathematical techniques and extraction of data from online sources into centralized storage.

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Imbalanced Datasets & Sampling Distributions

This test aids the recruiters in analyzing  candidate’s understanding of classification data set with skewed class proportion and statistics through repeated sampling.

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Optimizing Functions and Information Theory

This test helps recruiters in quantifying a candidate's ability of maximizing or minimizing a real function by choosing input values from an allowed set and computing the value of the function in data science.

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Data Wrangling and Correction

This test helps recruiters understand a candidate’s skill of uniting complex data sets for easy analysis and checking whether data is correct or inaccurate.

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Numerical Ability-Quantitative Aptitude

This test aids the recruiters in understanding candidates speed of the executing tasks related to handling of numbers.

Sample Questions

Choose from our 100,000+ question library or add your own questions to make powerful custom tests

Question types:

Multiple Option

Topic:

ACF, PACF

Difficulty:

Hard


Q 1. Which of the following ACF plots represents a stationary time series?

graph images
Both Plot 1 and Plot 2
Only Plot 1
Neither Plot 1 nor Plot 2
Only Plot 2

Question types:

Multiple Option

Topic:

Data Extraction and Mining - Genetic Algorithms

Difficulty:

Hard


Q 2. Suppose a genetic algorithm uses chromosomes of the form x = abcdefgh with a fixed length of eight genes. Each gene can be any digit between 0 and 9. Let the fitness of individual x be calculated as:


Let the initial population consist of four individuals with the following chromosomes:


Evaluate the fitness of each individual and arrange them in order with the fittest first and the least fit last.
None of these

Sample Report

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Skill wise performance report by iMocha

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