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What is this?
A free, community-built collection of data science interview questions with answers, stored as markdown files in this repo.
A free, community-built collection of data science interview questions with answers, stored as markdown files in this repo.Is it a structured course, or can I just dip into it as a reference?It's a reference you dip into, not a course — questions live in separate markdown files by category, so you can jump straight to what you want to practice.
It's a reference you dip into, not a course — questions live in separate markdown files by category, so you can jump straight to what you want to practice.Where should I start when preparing for an interview?Start with theory.md for conceptual questions and technical.md for SQL, Python and coding, then browse awesome.md for other interview resources.
Start with theory.md for conceptual questions and technical.md for SQL, Python and coding, then browse awesome.md for other interview resources.What topics do the questions cover?Theory covers linear models, trees and neural networks; technical covers SQL, Python and coding; a separate contrib folder adds probability questions.
Theory covers linear models, trees and neural networks; technical covers SQL, Python and coding; a separate contrib folder adds probability questions.Is the collection still being updated?Yes — the README promises more categories are coming, and the project is a joint effort of many people.
Yes — the README promises more categories are coming, and the project is a joint effort of many people.Can I reuse the material, say for a study group or a blog post?Yes, as long as you give credit — the work is published under Creative Commons Attribution 4.0 International (CC BY 4.0).
Yes, as long as you give credit — the work is published under Creative Commons Attribution 4.0 International (CC BY 4.0).Where can I discuss data interview topics with others?The author invites readers to join DataTalks.Club to talk about data, and posts project updates on Twitter and LinkedIn.
The author invites readers to join DataTalks.Club to talk about data, and posts project updates on Twitter and LinkedIn.Is this useful for senior candidates too, or mainly juniors?It spans fundamentals like linear models and trees alongside SQL, Python and coding practice, so it works both for learning the basics and as a broad refresher before senior interviews.
It spans fundamentals like linear models and trees alongside SQL, Python and coding practice, so it works both for learning the basics and as a broad refresher before senior interviews.What are similar projects?Waking-Up, machine-learning-interview, SDE-Interview-and-Prep-Roadmap, hello-algorithm, system-design-notes, llm_interview_note, node-interview, system-design-primer
Waking-Up, machine-learning-interview, SDE-Interview-and-Prep-Roadmap, hello-algorithm, system-design-notes, llm_interview_note, node-interview, system-design-primerWhich topics is it in?educational, neural-networks, study-guide, interview-questions, interview, interview-preparation, career, sql, data-science, reference, html, learning
educational, neural-networks, study-guide, interview-questions, interview, interview-preparation, career, sql, data-science, reference, html, learning