Data Scientist with Python Test

Candidates Assessed

12345+

Organizations Served

123+

Python Data Science coding test is for recruiters and hiring managers to assess the Python skills of a data scientist. Our Python Data Science coding test helped many enterprises to identify the right fit candidates and reduce time-to-hire by 40% & hiring costs by 45%.

About Data Scientist with Python Test

Data science experts have continued with their increasing development in Python for data analysis. This process mostly includes learning the Python fundamentals in the data science domain along with learning a few Python data science libraries for example NumPy, Pandas, Matplotlib, Scikit-Learn, etc. During this process, building a portfolio is also important i.e. by adapting Data Cleansing Projects, Data Visualization Projects, Machine Learning Projects, etc. Hence, Python is used at every step in the data science process for e.g. data scientists can use Python and Panda’s library to clean and sort the data into a data frame (table) which is ready for analysis, exploring, and visualizing the data.

Python Data Science coding test helps tech recruiters and hiring managers to assess candidates’ data science with Python skills. Data scientist with Python technical test is designed by experienced Subject Matter Experts (SME) to evaluate and hire data scientists with Python as per the industry standards.

Are you a jobseeker looking to sharpen your skills?

Test Duration: 60 minutes

No. of Questions: 23

Level of Expertise: Entry/Mid/Senior

Useful for hiring

  • Data Scientist using Python 
  • Python Data Analyst
  • Python Developer 
  • Machine Learning Developer   

Topics Covered


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Python and Libraries

Our Python Data Science test assesses a candidate's ability to work on python and also checks knowledge of libraries

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Data Mining, Data Wrangling, and Correction

iMocha's Python Data Science coding test evaluates the ability to data analysis, data mining & wrangling to draw the right conclusion and support decision making

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SQLite

The Python Coding Test For Data Science checks proficiency in SQLite coding to understand the candidate's knowledge of handling data

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Classification and Regression Algorithms

Python Data science test assesses a candidate's knowledge of Classification and Regression to evaluate the relationship between two or more independent variables

Sample Questions

Choose from our 100,000+ question library or add your own questions to make powerful custom tests

Question types:

Multiple Option

Topic:

Matplotlib

Difficulty:

Hard


Q 1. You are trying to plot a bar chart on a polar axis with given colors for every bar. You want to set the intensity of colors to 0.5. Which of the following is the correct way to do so?
Refer to the given sample bar chart on the polar axis with a color intensity of 0.5.


Bar chart image
polar_bar(theta, radii, width=width, color=colors, alpha=0.5)
bar(theta, radii, width=width, color=colors, alpha=0.5)
bar(theta, radii, width=width, color=colors, intensity=0.5)
polar_bar(theta, radii, width=width, color=colors, intensity=0.5)

Question types:

Multiple Option

Topic:

Classification and Regression Algorithms

Difficulty:

Easy


Q 2. Which of the given algorithms is most sensitive to outliers?
K-Means Clustering algorithm
K-Medians Clustering algorithm
K-Modes Clustering algorithm
K-Medoids Clustering algorithm

Sample Report

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Skill wise performance report by iMocha

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You can customize this test by

difficulty level
Setting difficulty level of test      

Choose easy, medium or hard questions from our skill libraries to assess candidates of different experience levels.                       

multiple skills
Combining multiple skills into one test

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Adding your own questions to the test

Add, edit or bulk upload your own coding questions, MCQ, whiteboarding questions & more.                       

tailor made test
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Trusted By

Rehana Nisar, Global Product & Services Recruitment Head, Gartner
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“We realized that to acquire quality talent, our recruitment process was in dire need of automation. Too many of our technical team were simply wasting hours conducting interviews that did not yield the desired results. For us, the foremost criteria was to find a recruitment partner who could tick all the right boxes.”

Rehana Nisar, Global Product & Services Recruitment Head,

Gartner

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