Data Science Skill Test

Skill summary

Using our curated data science skill test lets you measure how a data scientist converts complex data into actionable insights to inform business decisions and drive business value. 

Data scientists use statistical analysis and machine learning to build predictive models and communicate their findings to stakeholders, and collaborate with cross-functional teams to solve business problems. 


Multiple Choice Questions


10 min






Start with skill to build your data science dream team with Glider AI skill tests

Why we created this test

This test evaluates candidates’ knowledge on the fundamentals of data science and programming, and their skills in and theoretical understanding of statistics, machine learning, and neural networks and deep learning.

Candidates who do well on the data science skill test have a good grasp of fundamental and more advanced topics in data science. They can handle data well and work with large datasets to analyze scenarios and come to conclusions.

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Data Science Glider AI skill intelligence platform
Data Science Glider AI skill intelligence platform

Skills evaluated

Data scientists need to have certain competencies to do their job well. The skills that they should have are:
Fundamentals of coding languages like Python and R
Proficiency in statistics, mathematics, and quantitative analysis
Experience in AI, ML, NLP, linear algebra, and maths
Proficiency in data structure, algorithms, visualization, and interpretation
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Related roles

Use the data science skill test to hire for these roles:
Data Scientist
Big Data engineer
Data Architect
Data Security Analyst
Database Manager
Business Intelligence Analyst
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Data Science Glider AI skill intelligence platform

Science-backed questions for hundreds of roles

Use these sample questions to evaluate skill and fit for the data scientist role before hiring.

1. ______ command helps us present a message description
  • git command -m 
  • git command -d 
  • git command -message 
  • All of these 
  • None of these 
2. ______ is the goal of statistical modeling
  • Inference 
  • Summarizing 
  • Subsetting 
  • Moderation 
  • None of these 


3. ______ is a primary necessity in data science
  • Answer 
  • question 
  • Data 
  • All of these 
  • None of these
4. ______ is also known as data fishing
  • Data bagging 
  • Data booting 
  • Data merging 
  • All of these 
  • None of these 
5. ______ is the next step after data acquisition
  • Data integration 
  • Data cleansing 
  • Data replication 
  • Data modeling 
  • None of these 

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