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About the test

The General and Python Data Science and SQL online 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.

Sample public questions

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?

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

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
Python Data Science
Pandas
Easy
15 min
code
Public

Given the following data definition, write a query that returns the number of students whose first name is John.

TABLE students
   id INTEGER PRIMARY KEY,
   firstName VARCHAR(30) NOT NULL,
   lastName VARCHAR(30) NOT NULL
SQL
Aggregation
Select
Easy
3 min
code
Public

A table containing the students enrolled in a yearly course has incorrect data in records with ids between 20 and 100 (inclusive).

TABLE enrollments
  id INTEGER NOT NULL PRIMARY KEY
  year INTEGER NOT NULL
  studentId INTEGER NOT NULL

Write a query that updates the field 'year' of every faulty record to 2015.

SQL
Conditions
Update
Easy
5 min
code
Public

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61 more premium General and Python Data Science, and SQL questions

  Class Grades,   Age and Earnings,   Distribution Fitting,   Median Height,   Birthday Cards,   Billiard Club Occupancy,   Cheating Indicator,   Patient Classification,   Subscribers,   Wine Quality,   Distribution of Answers,   Hiring Process,   CTR,   Bank Loan,   Cubic Approximation,   Clean CSV,   Roads,   Authors,   Autocomplete,   Ban Users,   Bank Branches,   Cheapest Product,   Countries,   Developers,   Hospital Patients,   Index Performance,   Menu Items,   Movies,   Projects,   Rectangles,   Restaurant Menu,   Retirees,   SMS Messages,   Poll,   Users and Accounts,   Matching Clothes,   Agents,   Sales,   Employee Manager,   Auto Show,   Delete Orders,   Welfare Organization,   Tasks,   Manager Sales,   Student Rankings,   Student Activities,   Youngest Child,   Movies Live,   Department Report,   Ingredients,   Average Salary,   Movie Genres,   Rain,   River Discharge,   Credit Wizard,   Bacterial Growth,   ATM Locations,   Credit Score,   Transactions,   Free Throws,   Merge Stock Index.

Skills and topics tested

  • Python Data Science
  • Grouping
  • NumPy
  • Pandas
  • General Data Science
  • Linear Regression
  • Cauchy Distribution
  • Exponential Distribution
  • Normal Distribution
  • SciPy
  • Data Cleaning
  • Sorting
  • Outliers
  • Bayes' Theorem
  • Probability
  • Classification
  • Decision Boundary
  • Poisson Distribution
  • Correlation
  • Multicollinearity
  • Probability Distributions
  • Decision Tree
  • Machine Learning
  • Binomial Distribution
  • P-Value
  • Nonlinear Regression
  • Scikit-Learn
  • Processing CSV
  • SQL
  • Conditions
  • Views
  • Select
  • Create Index
  • Database Schema
  • Performance Tuning
  • Alter Table
  • Constraints
  • Create Table
  • Aggregation
  • Subqueries
  • Ordering
  • Bug Fixing
  • Indexes
  • Group by
  • Update
  • Insert
  • Joins
  • Left Join
  • Right Join
  • Delete
  • SQL CASE
  • Union
  • CTE
  • ROC
  • Database Normalization
  • Curve Fitting
  • K-Nearest Neighbors
  • Data Aggregation

For job roles

  • Data Analyst
  • Data Scientist

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