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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.

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Free Sample Questions for General and Python Data Science, Python, and SQL Test

Our sample questions are free for companies to use on a trial plan. See more about our premium questions for paid plans below.

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?

Petri Dish

5min
  -  
Easy  
  -  
MMCQ

General Data Science Correlation Public

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

Election Poll

20min
  -  
Easy 
  -  
FILL

General Data Science Descriptive statistic Exploratory Data Analysis Public New

Each day during 2019 an agency asked a hundred randomly selected people which party they would vote for if elections were held that day. Results of the poll were recorded in the following file. The Workers' Party asked for the report which they plan to use to improve their strategy for upcoming elections.

Fill in the missing values in the report for 2019:

  • The arithmetic mean of votes for the Workers' Party is: __ (rounded to one decimal place)
  • The median of votes for the Workers' party is: __ (rounded to closest integer)
  • The standard deviation of votes for the Workers' party is: __ (rounded to one decimal place)
  • The difference between the largest and the smallest number of votes for the Workers' party for March is: __
  • The largest number of votes that any party received on any day is: __ votes.
    That maximum was achieved on 2019-__-__ by __.
  • The party with the largest difference between the maximum and minimum number of votes is _______. That difference is __ votes.
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Premium Questions for General and Python Data Science, Python, and SQL Test

TestDome offers a premium questions library with 1000+ unique, hand-crafted questions whose answers can’t be found online. If you spot an answer somewhere online, we’ll give you a refund.

Question Names

These premium questions are included in this pre-built test and can be added to any multi-skill test. Please sign up for a paid plan to view the questions in detail.

Age and Earnings, CTR, Bacterial Growth, Distribution of Answers, Bank Loan, Hiring Process, Billiard Club Occupancy, Cheating Indicator, Credit Wizard, Patient Classification, Rain, Subscribers, Wine Quality
Covered Skills

Testing of these skills is covered in this pre-built test because they’re closely related. On our paid plan, you can easily create your own custom multi-skill tests.

General Data Science Linear regression Binomial distribution Probability p-value Curve Fitting Probability distributions Classification Machine learning Decision tree Outliers Bayes' theorem Decision boundary ROC Poisson distribution Correlation Multicollinearity
Recommended Job Roles

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Data Analyst

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