Data Science Test

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The Data Science test assesses a candidate’s ability to analyze data, extract information, suggest conclusions, and support decision-making, as well as their ability to take advantage of Python and its data science libraries such as NumPy, Pandas, or SciPy.

It's the ideal test for pre-employment screening. Data scientists and data analysts who use Python for their tasks should be able to  leverage the functionality provided by Python data science 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 to write programs using Python for the same purpose.

Recommended Job Roles
Data Analyst
Data Scientist
Statistician
Sample Candidate Report

Sample Free Questions

Pet Detection

5min
  -  
Easy  
  -  
NUM

General Data Science Confusion matrix Machine learning Public

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?

Petri Dish

5min
  -  
Easy  
  -  
MMCQ

General Data Science Correlation Public New

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

Login Table

15min
  -  
Easy 
  -  
CODE

Python Data Science Pandas Public New

A company stores login data and password hashes in two different containers:

  • DataFrame with columns: Id, Login, Verified.
  • Two-dimensional NumPy array where each element is an array that contains: Id and Password.

Elements on the same row/index have the same Id.

Implement the function login_table that accepts these two containers and modifies id_name_verified DataFrame in-place, so that:

  • The Verified column should be removed.
  • The password from NumPy array should be added as the last column with the name "Password" to DataFrame.

For example, the following code snippet:

id_name_verified = pd.DataFrame([[1, "JohnDoe", True], [2, "AnnFranklin", False]], columns=["Id", "Login", "Verified"])
id_password = np.array([[1, 987340123], [2, 187031122]], np.int32)
login_table(id_name_verified, id_password)
print(id_name_verified)

Should print:

   Id        Login   Password
0   1      JohnDoe  987340123
1   2  AnnFranklin  187031122
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Premium Questions

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Questions
Class Grades, Subscribers, Age and Earnings, Cubic Approximation, Credit Score, Rain, Patient Classification, CTR, Distribution Fitting, Wine Quality, Credit Wizard, Median Height, Clean CSV, Birthday Cards, Free Throws, Bacterial Growth
Skills
Python Data Science Grouping NumPy Pandas General Data Science Poisson distribution Probability Linear regression Machine learning Nonlinear regression Scikit-learn Classification k-nearest neighbors ROC Decision boundary Binomial distribution p-value Cauchy distribution Exponential distribution Normal distribution SciPy Correlation Multicollinearity Decision tree Data cleaning Processing CSV Sorting Data aggregation Curve Fitting
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