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About the test

The General and Python Data Science, Python, 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 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.

Sample public questions

5 min
General Data Science
Confusion Matrix
Machine Learning

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?

3 min

A university's Office of Admission keeps track of student majors. Each student can have a single major. Below is an example of how their system stores students, majors, and how it manipulates them:

students = [("Allen Anderson", "Computer Science"),
            ("Edgar Einstein", "Engineering"),
            ("Farrah Finn", "Fine Arts")]
def add_new_student(students, name, major):
    students.append((name, major))

def update_student(students, index, name, major):
    students[index] = (name, major)

def find_students_by_name(students, name):
    return [student for student in students if name in student[0]]

def get_all_majors(students):
    return [student[1] for student in students]

What can be concluded from the code snippet above?

15 min
Python for Data Science

A company stores login data and passwords 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
3 min

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

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

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

TABLE enrollments

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

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

Document Store, Is Alpha, Shipping, Log Patch, Category Tree, Paragraph, Products, Username, Language Teacher, Greeter, Tuple Slice, Medical Record, Hobbies, Class Grades, Age and Earnings, Distribution Fitting, Median Height, Billiard Club Occupancy, Procedural Generator, Cheating Indicator, Patient Classification, Subscribers, Wine Quality, Kilometer Converter, Crop Ratio, Log Parser, Cargo Ship, String Occurrence, Random Playlist, File Format, Book Authors, Count Hobbies, Chain Link, Distribution of Answers, Hiring Process, CTR, XML Navigation, Bank Loan, Cubic Approximation, Clean CSV, Roads, Authors, Autocomplete, 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, Segment, Rain, River Discharge, Credit Wizard, Bacterial Growth, ATM Locations, Ceramic Store, Action Stack, Read Write Execute, Read First Line, Transactions, Book Sale, Unique Numbers, Moving Total, Max Sum, Internal Nodes, Friend, Merge Stock Index, Date Transform, Ban Users, Reward Points, Veterinarian, Numbers To Text, Worker, Node, Chinese Box, Driver Exam, Stories, Car Rental, Paper Strip, Birthday Cards, Free Throws, Credit Score, Flight Connections, Vectors, Special Actions.

Skills and topics tested

  • Python
  • Bug Fixing
  • Language
  • List Comprehension
  • Strings
  • Arithmetic
  • Monkey Patching
  • Dictionary
  • Exceptions
  • Regex
  • JSON
  • Serialization
  • Sorting
  • Inheritance
  • OOP
  • Tuples
  • Named Tuple
  • Iteration
  • Lists
  • Python for Data Science
  • Grouping
  • NumPy
  • Pandas
  • General Data Science
  • Linear Regression
  • Cauchy Distribution
  • Exponential Distribution
  • Normal Distribution
  • SciPy
  • Data Cleaning
  • Outliers
  • Random
  • Bayes' Theorem
  • Probability
  • Classification
  • Decision Boundary
  • Poisson Distribution
  • Correlation
  • Multicollinearity
  • Method Overriding
  • Integer Division
  • XML
  • Stream
  • Closures
  • Multithreading
  • Performance Tuning
  • Conditional Statements
  • Linked List
  • Probability Distributions
  • Decision Tree
  • Machine Learning
  • Binomial Distribution
  • P-Value
  • Dynamic Objects
  • Nonlinear Regression
  • Scikit-Learn
  • Processing CSV
  • SQL
  • Conditions
  • Views
  • Select
  • Create Index
  • Database Schema
  • Constraints
  • Create Table
  • Aggregation
  • Subqueries
  • Ordering
  • Indexes
  • Alter Table
  • Group by
  • Update
  • Insert
  • Joins
  • Left Join
  • Right Join
  • Delete
  • Union
  • CTE
  • ROC
  • Database Normalization
  • Curve Fitting
  • Stack
  • Algorithmic Thinking
  • Set
  • Tree
  • Graphs
  • Queue
  • Memory Management
  • Recursion
  • Data Structures
  • Dynamic Programming
  • Data Aggregation
  • K-Nearest Neighbors

For job roles

  • Data Analyst
  • Data Scientist

Sample candidate report

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