The Data Science test assesses a candidate’s ability to analyze data, extract information, suggest conclusions, and support decision-making.

It's the ideal test for pre-employment screening. Data scientists, data analysts, and statisticians need to be able to extract 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
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Sample Free Questions

Petri Dish


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

AB Test


Data Science Bayes' theorem Probability Public New

Your company is running a test that is designed to compare two different versions of the company’s website.

Version A of the website is shown to 60% of users, while version B of the website is shown to the remaining 40%. The test shows that 8% of users who are presented with version A sign up for the company’s services, as compared to 4% of users who are presented with version B.

If a user signs up for the company’s services, what is the probability that she/he was presented with version A of the website?

Login Table


Data Science Python data libraries 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)

Should print:

   Id        Login   Password
0   1      JohnDoe  987340123
1   2  AnnFranklin  187031122
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Age and Earnings, Subscribers, Credit Wizard, Clean CSV, Median Height, Free Throws, Credit Score, Birthday Cards, CTR, Class Grades, Bacterial Growth, Distribution Fitting
Data Science Linear regression Poisson distribution Probability Decision tree Python data libraries Data aggregation k-nearest neighbors Sorting Binomial distribution p-value Curve Fitting Cauchy distribution Exponential distribution Normal distribution
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