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youssefhosni/data-science-interview-questions-answers — explained in plain English

Analysis updated 2026-06-26

5,627Audience · dataComplexity · 1/5Setup · easy

In one sentence

A curated collection of data science interview questions and answers organized by topic, covering ML, deep learning, statistics, Python, SQL, and more.

Mindmap

mindmap
  root((ds-interview-qa))
    Topics covered
      Machine learning
      Deep learning
      Large language models
      Computer vision
      Statistics and probability
    Practical skills
      Python patterns
      SQL query design
      Resume presentation
    Format
      Markdown files
      GitHub hosted
      Medium articles
    Community
      LinkedIn series
      Pull request contributions
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Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

What do people build with it?

USE CASE 1

Study the machine learning and statistics sections before a data science job interview to review conceptual questions.

USE CASE 2

Practice SQL and Python coding question formats that commonly appear in technical screening rounds.

USE CASE 3

Review the resume-based questions section to learn how to present past projects and experience convincingly.

USE CASE 4

Contribute new interview questions via pull request to help other candidates preparing for data science roles.

What is it built with?

Markdown

How does it compare?

youssefhosni/data-science-interview-questions-answersapppear/chartviewdeeplabcut/deeplabcut
Stars5,6275,6275,627
LanguageSwiftPython
Setup difficultyeasyeasyhard
Complexity1/52/54/5
Audiencedatadeveloperresearcher

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min
License information was not mentioned in the explanation.

So what is it?

Data-Science-Interview-Questions-Answers is a curated collection of interview questions and answers for people preparing for data science roles. The content started as a daily LinkedIn series that the author began in May 2022, posting a new question each day and then summarizing community responses the following day. Those questions and answers are compiled here so readers can review them without following the LinkedIn feed. The questions are organized into separate Markdown files by topic. Categories covered include machine learning, deep learning, large language models, computer vision (split across three separate parts), statistics, probability, Python programming, SQL and databases, and resume-based questions. Each category links either to a Markdown file in the repository or to a Medium article that covers the same content in a slightly different format. The questions span both conceptual and practical territory. Conceptual questions ask how specific algorithms work, what the difference between related techniques is, or when to choose one approach over another. Practical questions cover Python coding patterns, SQL query design, and database concepts. Resume-based questions address how to present your work experience and projects in a data science context. Questions were contributed both by the author and by members of the author's LinkedIn network. The project accepts pull requests so the community can add new questions over time. The README also provides the author's contact details for anyone who wants to submit questions directly. There is no executable code in this repository. The content consists entirely of Markdown files intended to be read on GitHub or downloaded for study before an interview.

Copy-paste prompts

Prompt 1
Using the data-science-interview-questions-answers ML section as a guide, quiz me on 10 questions about supervised learning, bias-variance tradeoff, and model evaluation.
Prompt 2
Based on the statistics and probability section of this repo, explain the top 5 concepts I must know cold before a data science interview at a tech company.
Prompt 3
Help me answer the resume-based interview question 'describe a machine learning project you are proud of' using the structure suggested in this repo.
Prompt 4
From the SQL section of data-science-interview-questions-answers, give me 5 practice queries of increasing difficulty and check my answers.

Frequently asked questions

What is data-science-interview-questions-answers?

A curated collection of data science interview questions and answers organized by topic, covering ML, deep learning, statistics, Python, SQL, and more.

What license does data-science-interview-questions-answers use?

License information was not mentioned in the explanation.

How hard is data-science-interview-questions-answers to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is data-science-interview-questions-answers for?

Mainly data.

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