General and Python Data Science, Python, and SQL Online Test

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The General and Python Data Science, Python, 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 standard library along with 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 use Python for their tasks should be able  to leverage the functionality provided by Python' standard and data science libraries to extract and analyze knowledge and insights from data. 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, solve coding problems in Python, and write SQL queries that extract and combine data.

Recommended Job Roles
Data Analyst
Data Scientist
Sample Candidate Report

Sample Free Questions

Students

3min
  -  
Easy  
  -  
CODE

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
   id INTEGER PRIMARY KEY,
   firstName VARCHAR(30) NOT NULL,
   lastName VARCHAR(30) NOT NULL

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?

File Owners

10min
  -  
Easy  
  -  
CODE

Python Dictionary Public

Implement a group_by_owners function that:

  • Accepts a dictionary containing the file owner name for each file name.
  • Returns a dictionary containing a list of file names for each owner name, in any order.

For example, for dictionary {'Input.txt': 'Randy', 'Code.py': 'Stan', 'Output.txt': 'Randy'} the group_by_owners function should return {'Randy': ['Input.txt', 'Output.txt'], 'Stan': ['Code.py']}.

Login Table

15min
  -  
Easy 
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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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Python Bug fixing Language List comprehension Strings Arithmetic Exceptions Monkey patching SQL Conditions Select Dictionary Linked list Aggregation Subqueries Ordering Left join Union Group by Insert Joins Serialization XML Regex JSON Sorting Inheritance OOP CTE SQL CASE Algorithmic thinking Set Tuples Lists Tree Method overriding Stream Queue Named tuple Iteration Integer division 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 Data aggregation Curve Fitting Performance tuning
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