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

Easy
15 min
code
Public
Python for Data Science
Pandas

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)
print(id_name_verified)

Should print:

   Id        Login   Password
0   1      JohnDoe  987340123
1   2  AnnFranklin  187031122
Easy
20 min
code
Public
Python for Data Science
Classification
Machine Learning
NumPy
Scikit-Learn

As a part of an application for iris enthusiasts, implement the train_and_predict function which should be able to classify three types of irises based on four features.

The train_and_predict function accepts three parameters:

  • train_input_features - a two-dimensional NumPy array where each element is an array that contains: sepal length, sepal width, petal length, and petal width.
  • train_outputs - a one-dimensional NumPy array where each element is a number representing the species of iris which is described in the same row of train_input_features. 0 represents Iris setosa, 1 represents Iris versicolor, and 2 represents Iris virginica.
  • prediction_features - two-dimensional NumPy array where each element is an array that contains: sepal length, sepal width, petal length, and petal width.

The function should train a classifier using train_input_features as input data and train_outputs as the expected result. After that, the function should use the trained classifier to predict labels for prediction_features and return them as an iterable (like list or numpy.ndarray). The nth position in the result should be the classification of the nth row of the prediction_features parameter.

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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

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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.

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