Data Science and Analytics Test Assessment

Topics covered

Introduction to Data Science, Analytics with R & Tools, Data Manipulation using R, Exploratory Data Analysis, Introduction to Statistics, Regression Analysis, Machine Learning Techniques using R, Data Visualization

  • MAQ (Multiple Answer Question)
  • MCQ (Multiple Choice Question)
  • Descriptive Question
  • White Board Simulator
  • Coding Simulator
  • Audio Question
  • Video Question
  • Case Study Question

Data Science test is useful to effectively assess the skills of candidates before an interview. Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms which includes data analysis fields such as statistics, machine learning, and predictive analysis. It is designed to check the analytics and data science methods. The test is created and validated by Subject Matter Experts. 

The Data Science Test contains questions on following topics: 

  • Introduction to Data Science

  • Analytics with R & Tools

  • Data Manipulation using R

  • Exploratory Data Analysis

  • Introduction to Statistics

  • Regression Analysis

  • Machine Learning Techniques using R

  • Data Visualization

This pre-employment test is useful for hiring:
  • Data Science Engineer

Test details:

Data Science Test enables employers and recruiters to evaluate skills and job readiness of candidates. For this reason, the emphasis is laid upon evaluating the knowledge of applied skills gained through real work experience, rather than theoretical knowledge.

Test Details: 20 minutes 

20 Application Questions


The combination of Application and Theory questions helps to evaluate Technical as well as practical Skills of Candidates.

Data Science and Analytics Test

Question #1 of 3

Which statements are true?

Note: There can be multiple correct answers to this question.

    • Simple linear regression is equipped to handle more than one predictor

    • Compound linear regression is not equipped to handle more than one predictor

    • Linear regression consists of finding the best-fitting straight line through the points

    • None of the mentioned

Question #2 of 3

What is the result of following code for f(1)?

b <- 4
f <- function (a)
{
b <- 5
b-3 + g (a)
}
g <- function (a)
{
a+b
}

    • 4

    • 1

    • 7

Question #3 of 3

Suppose I want to extract the first column of each matrix in the list. How I can do that?

    • zapply(x, function(elt) { elt[,1] })

    • lapply(x, function(elt) { elt[,1] })

    • extract(x, function(elt) { elt[,1] })

SAMPLE REPORT
Candidate Name: David Messi Test Date: 28-May-2014
Test Name: Data Science and Analytics Test Test Start Time: 13.30

Score: 12 Out Of 20

Percentage: 60

Result: PASS

passing score image
SECTION PERFORMANCE
Section Name FAIL PASS
Introduction to Data Science
Score: 3/3
Analytics with R & tools
Score: 1/3
Data Visualization
Score: 2/3
Machine Learning Techniques using R
Score: 3/3
Exploratory Data Analysis
Score: 1/3
Data Manipulation using R
Score: 2/5
STRENGTH AND WEAKNESS
Strength Introduction to Data Science, Machine Learning Techniques using R
Weakness Analytics with R & tools, Exploratory Data Analysis, Data Manipulation using R
* Note: If score <= 40 then its a Weakness. If score >= 80 then its a Strength.
ONLINE PROCTORING
0%
10 20 30 40 50 60 70 80 90 100%
Tolerable Limit Not acceptable
Window Violation: 0 | Time Violation: 0 secs
CANDIDATE DETAILS
Gender : Male Phone Number : +1-541-754-3010
Total Experience (Years) : 3 Total Experience (Months) : 5
Skill Set : Introduction to Data Science, Machine Learning Techniques using R Current Employer : Interview Mocha
Current Job Title : Solution Architect Current Salary : 600.00
Expected Salary : 1000000.00 Current Location : Chicago

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