This R and General Data Science Online Test Separates Good From Bad Hires

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About the test

The General Data Science and R online test assesses a candidate’s ability to analyze data, extract information, suggest conclusions, and support decision-making. It also tests a candidate’s knowledge of programming in the R language and their ability to leverage commonly used built-in functions.

It's the ideal test for pre-employment screening. Data scientists and data analysts who use R for their tasks should be able to leverage the functionality provided by R’s standard libraries to extract and analyze knowledge and insights from data.

This test requires candidates to demonstrate their ability to apply probability and statistics when solving data science problems, and solve coding problems in the R language.

Sample public questions

Easy
5 min
num
Public
General Data Science
Confusion Matrix
Machine Learning

A classifier that predicts if an image contains only a cat, a dog, or a llama produced the following confusion matrix:

  True values    
Dog Cat Llama
Predicted values     Dog 14 2 1
Cat 2 12 3
Llama 5 2 19

What is the accuracy of the model, in percentages?

Easy
10 min
code
Public
R
Arithmetic

Implement the function findRoots to find the roots of the quadratic equation: ax2 + bx + c = 0. The function should return a vector containing roots in any order. If the equation has only one solution, the function should return that solution as both elements of the vector. The equation will always have at least one solution.

The roots of the quadratic equation can be found with the following formula: A quadratic equation.

For example, findRoots(2, 10, 8) should return a vector containing (-1, -4) or (-4, -1) as the roots of the equation 2x2 + 10x + 8 = 0 are -1 and -4.

Easy
5 min
mmcq
Public
General Data Science
Correlation

Two bacteria cultures, A and B, were set up in two different dishes, each covering 50% of its dish. Over 20 days, bacteria A's percentage of coverage increased to 70% and bacteria B's percentage of coverage reduced to 40%:

Petri Dish

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Skills and topics tested

  • General Data Science
  • Linear Regression
  • Outliers
  • Bayes' Theorem
  • Probability
  • Classification
  • Decision Boundary
  • Poisson Distribution
  • Correlation
  • Multicollinearity
  • Probability Distributions
  • Decision Tree
  • Machine Learning
  • Binomial Distribution
  • P-Value
  • ROC
  • Curve Fitting
  • R
  • Data Cleaning
  • DataFrame
  • Arithmetic
  • Iteration
  • Vector
  • Grouping

For job roles

  • Data Analyst
  • Data Scientist
  • Statistician

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