Python Data Science Online Test

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About the test

The Python Data Science online test assesses knowledge of using Python and data science libraries such as Pandas, NumPy, Scipy, and Scikit-learn to analyze data through a series of live coding questions. This test requires applying probability and statistics to solve data science problems.

The assessment includes work-sample tasks such as:

  • Classification of data using different algorithms.
  • Aggregating, grouping, sorting, and cleaning data.
  • Building machine learning models.

A good data scientist or data analyst using Python for their tasks should be able to take advantage of the functionality provided by Python data science libraries to extract and analyze knowledge and insights from data.

Sample public questions

Hard
30 min
code
Public
AI-resistant
Python for Data Science
Linear Regression
Machine Learning
NumPy
Scikit-Learn

Implement the desired_marketing_expenditure function, which returns the required amount of money that needs to be invested in a new marketing campaign to sell the desired number of units.

Use the data from previous marketing campaigns to evaluate how the number of units sold grows linearly as the amount of money invested increases.

For example, for the desired number of 60,000 units sold and previous campaign data from the table below, the function should return the float 250,000.

Previous campaigns

Campaign Marketing expenditure Units sold
#1 300,000 60,000
#2 200,000 50,000
#3 400,000 90,000
#4 300,000 80,000
#5 100,000 30,000
Hard
30 min
code
Public
Python for Data Science
Correlation
NumPy
Pandas

You are given a list of tickers and their daily closing prices for a given period.

Implement the most_corr function that, when given each ticker's daily closing prices, returns the pair of tickers that are the most highly (linearly) correlated by daily percentage change.

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8 more premium Python Data Science questions

Class Grades, Distribution Fitting, Median Height, Cubic Approximation, Clean CSV, Birthday Cards, Free Throws, Credit Score.

Skills and topics tested

  • Python for Data Science
  • Grouping
  • NumPy
  • Pandas
  • Cauchy Distribution
  • Exponential Distribution
  • Normal Distribution
  • SciPy
  • Data Cleaning
  • Machine Learning
  • Nonlinear Regression
  • Scikit-Learn
  • Processing CSV
  • Sorting
  • Data Aggregation
  • Classification
  • K-Nearest Neighbors

For job roles

  • Data Analyst
  • Data Scientist
  • Statistician

Sample candidate report

What others say

Decorative quote

Simple, straight-forward technical testing

TestDome is simple, provides a reasonable (though not extensive) battery of tests to choose from, and doesn't take the candidate an inordinate amount of time. It also simulates working pressure with the time limits.

Jan Opperman, Grindrod Bank

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Capterra - Shortlist (2023)

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