General and Python Data Science, and SQL Online Test

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The General and Python Data Science and SQL 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 also tests a candidate’s knowledge of SQL queries and relational database concepts.

It's the ideal test for pre-employment screening. Data scientists and data analysts who are using Python for their tasks should be able to leverage the functionality provided by Python data science libraries to extract and analyze knowledge and insights. Often, they also need a solid understanding of SQL to interface and access an SQL database efficiently.

This test requires candidates to demonstrate their ability to apply probability and statistics when solving data science problems, write programs using Python for the same purpose, and write SQL queries that extract and combine data.

Recommended Job Roles
Data Analyst
Data Scientist
Sample Candidate Report

Sample Free Questions



SQL Aggregation Select Public

Given the following data definition, write a query that returns the number of students whose first name is John. String comparisons should be case sensitive.

TABLE students
   firstName VARCHAR(30) NOT NULL,
   lastName VARCHAR(30) NOT NULL

Pet Detection


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?

Login Table


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)

Should print:

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

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Authors, Cheapest Product, Countries, Developers, Employee Manager, Merge Stock Index, Movies, Movies Live, Rectangles, Retirees, Sales, Student Activities, Tasks, Transactions, Youngest Child, Manager Sales, Average Salary, Movie Genres, Auto Show, Poll, 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, Student Rankings, Welfare Organization, Bacterial Growth, Student Max Score
SQL Conditions Select Aggregation Subqueries Ordering Bug fixing Left join Union Group by Insert Joins CTE SQL CASE 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 Performance tuning
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