Skip to main content

Welcome to our Data Science Interview Course!

Data science can be a challenging and wide-ranging field, and the interviews you'll have as a data scientist are no exception. We designed this course to cover everything you'll need to know.

Interviewers are looking for "unicorns" who have both technical and business communications skills. Successful candidates know how they can contribute to the company, take initiative, and leverage data to solve business problems.

How we made this course

We've partnered with senior data scientists and hiring managers from top tech companies who have conducted hundreds of data science interviews. Together, we've built this course around their hiring rubrics, common question patterns, and the practical knowledge you'll need on interview day.

After coaching hundreds of candidates, we've found that nothing replaces deliberate practice. That's why the Aced membership includes:

  • Practice questions across every type of data science interview round, from SQL and statistics to case studies and behavioral.
  • A dynamic, interactive format for self-guided practice.
  • Hundreds of data science lessons, questions, and answers.
  • Unique guides and frameworks for SQL, A/B testing, ML concepts, and case studies.
  • A peer-to-peer mock interview platform for live practice with other candidates.
  • Access to our Discord community for support throughout your prep.

Who this course is for

This course is for candidates applying to data science roles who already have a working foundation in statistics, SQL, and ML and want a structured way to prepare for interviews.

Data scientist roles vary widely across companies, from product analytics to applied ML to experimentation-heavy growth work. The course is designed for comprehensive preparation, with the flexibility to focus on the specific rounds and topics that matter most for your target roles.

Reports from data science candidates interviewing at companies including Google, Meta, DoorDash, OpenAI, and others show a few consistent shifts in what interviewers care about:

  • SQL is a hard gating round, not a warm-up. Candidates have reported timed SQL screens where interviewers are focused on whether you produce the correct query, not on hearing you narrate your logic. Window functions, ranking, and gotchas like dense_rank and seasonality are showing up regularly.
  • Case studies and product sense carry significant weight. Many recent loops lean heavily on metric definition, experiment interpretation, and business framing. Candidates are increasingly asked to define success for a feature, reason about red and green metrics together, and connect analysis back to product decisions.
  • AI and LLM topics are surfacing inside DS loops. Some candidates have been asked to set up a RAG system, defend a fine-tuning choice, or read and debug AI-generated SQL or Python in real time. These appear alongside the standard rounds, not as a replacement for them.
  • Project depth is under more scrutiny. Interviewers are pushing past surface-level project summaries to ask why you chose ML over a simpler baseline, what tradeoffs you made on evaluation and inference cost, and what you would do differently. Senior loops in particular expect multiple distinct projects with clear scope and impact.
  • LeetCode-style algorithms are usually not central. Most DS loops still test light coding, ML coding, or applied analytics rather than competitive programming. Strong SQL and applied ML fundamentals matter far more here than puzzle-style problems.

These shifts make the fundamentals more important, not less. A clean grasp of statistics and experimentation makes case studies easier to handle. A strong ML foundation makes it easier to defend an LLM or RAG decision under follow-up. The rest of this course is built to give you both: the core skills every DS loop tests, plus the context for the newer topics showing up today.

What you'll learn

By the end of the course, you'll know:

  • What interview questions to expect in each round and how to answer them effectively
  • Specific data science topics to study and review
  • Common pitfalls to avoid in interviews
  • Specific expectations for senior level candidates
  • How to communicate with technical and non-technical interviewers

Here's an overview of what this course covers:

Once you've worked through the lessons, keep practicing in our interview question database. We select the top answers to give detailed feedback and analysis.

Next Lesson