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

30 min
Python for Data Science

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.

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

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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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