Clock
Test duration:
60 minutes
min
Notes
No. of questions:
23
Tie
Level of experience:
Entry Level/Mid/Expert

Data Science Aptitude Test

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.  


15645
+
Candidates
Assessed
143
+
Organizations Served
Fujitsu
Paypal
Capgemini
Deloitte
Coupa
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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.  

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How it works

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.

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

Question types :

Multiple Option

Topic:

Difficulty:

Hard


Q 1. You have been asked to train « Naive Bayes model for spam detection dataset

Using Python command, what wil you add in the given code at XXXX to deciore, it and train your modal for the given dataset?

import numpy os np

X= nporroyll 3. 2b[1 21 [2.21.[3,21)
Y= nporray(fi.a,a.1))

XXXX

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

Question types :

Multiple Option

Topic:

Machine teaming

Difficulty:

Hard


Q 1. Q2 Lets assume that you have 6 Pandas DataFrome(af) in the below tole.

Index Code Open High
0 AAPL 19877 70074
1 AAPL 19877 70074
2 AAPL 19877 70074
3 AAPL 19877 70074
4 AAPL 19877 70074

When the following commend is executed, what hoppens to the DatoFrome?

df= defiinat()
Y= nporray(fi.a,a.1))

Index Code Open High
0 AAPL 19877 70074
1 AAPL 19877 70074
2 AAPL 19877 70074
3 AAPL 19877 70074
4 AAPL 19877 70074
Index Code Open High
0 AAPL 19877 70074
1 AAPL 19877 70074
2 AAPL 19877 70074
3 AAPL 19877 70074
4 AAPL 19877 70074
it gives an error becouse of the third row which has on empty string.
None of these

Question types :

Multiple Option

Topic:

ACF, PACF

Difficulty:

Hard

Question:

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
A helicopter view of the employee's progress
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FAQ
What is Data Science?
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Why use iMocha’s online data science test?
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What are the key skills that recruiter should look for while hiring a Data Scientist?
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What are few interview questions that recruiter/hiring manager should ask while hiring a data scientist?
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How do you assess the data scientists?
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