Best Data Science Interview Prep Platforms and Resources (2026)

The right prep platform depends on the role you want and the round you are preparing for, so most candidates end up combining one question bank, one way to practice out loud, and a book or two. LeetCode is built around algorithm and coding problems, DataLemur and StrataScratch focus on SQL and data coding practice, Aced (formerly Exponent) is a course-and-mock platform across product, engineering and data roles, and Interview Query is built only for data roles: SQL, statistics, product cases, machine learning and behavioral rounds, plus company interview guides and AI mock interviews. If your loop is a data science, analytics, data engineering or ML loop, start with the rounds you will actually face and pick tools for those rounds.

Disclosure: I run Interview Query, one of the platforms compared here. Every fact about another company below was checked on that company's own website on November 11, 2026. Where we could not check a fact, we left it out.

• You have a data science or product analytics loop with SQL, stats, A/B testing and a product case: a data-specific question bank plus mock interviews. This is what Interview Query is designed for.
• You need to drill SQL fast before a screen: a focused SQL practice site such as DataLemur or StrataScratch, or the SQL practice on Interview Query. Our roundup of SQL practice platforms for every level compares the options, from beginner tutorials to interview-style simulators.
• Your loop includes classic algorithm rounds (software engineering, many ML engineering loops): LeetCode-style practice. We explain where that helps and where it does not in why LeetCode alone is the wrong prep for data science interviews.
• You learn best from video courses across several roles: Aced (formerly Exponent).
• You need to practice speaking under pressure: an AI mock interviewer for repetition, plus at least one human mock before the real thing.
• You want depth on concepts, not just questions: one interview book and one domain book.

We looked at each option the way a candidate does: which interview rounds it covers, which roles it is built for, what you can use for free, what a paid plan adds, and whether you can practice out loud. Product facts about Interview Query come from our live site and public API on November 11, 2026. Competitor facts come only from each competitor's own website on the same date. We did not score or rank platforms on subjective scales, and we do not repeat prices or counts we could not confirm today. Prices change often and sales are common, so check each pricing page before you buy.

LeetCode's site blocked automated access when we checked, so we could not verify its current plans or problem counts. Our Interview Query vs LeetCode comparison covers the differences in focus; confirm prices on leetcode.com.

Interview Query covers the rounds that show up in data loops: SQL, Python, statistics and probability, product metrics, A/B testing, machine learning, case studies and behavioral questions. As of November 11, 2026 the question bank has 980 interview questions you can filter by topic, company and difficulty, and our company pages draw on 38,000+ candidate interview experiences.

What you get for free: a free account lets you read question prompts, preview content and run your first AI mock interview, where you talk through a product case, statistics, SQL, Python, ML or behavioral question with an AI interviewer and get a scored report.

What needs a subscription (list prices on our pricing page as of November 11, 2026):

• IQ Coder ($29/month or $120/year): 300+ Python and SQL questions with solutions, unlimited code submissions, 3 AI interviews a month, peer-to-peer mock interviews and limited company guides.
• IQ Premium ($79/month, $199/year, or $299 one-time for lifetime access): everything in Coder, plus full access to company interview guides, 500+ case, system design and behavioral questions, 10 AI interviews a month, 10+ courses and answer walkthroughs. You can add 1:1 coaching to a yearly plan. See what Premium includes.

Premium includes 10 structured learning paths, from SQL to probability. If you want a narrower starting point, Study Plans give you 50 handpicked questions per role with progress tracking. For open-ended answers, our case study solution checker grades your written answer against an expert benchmark instead of leaving you to compare against community posts, and performance scores rank your skills against other candidates after a short multiple-choice assessment.

Does structured prep make a difference? We looked at career outcomes for 1,300+ alumni of one master's program: 44.8% of Interview Query users reached employers the study classed as elite or top tier, compared with 36.1% of non-users. That is a correlation, not proof of cause, and the study says so.

Many candidates know LeetCode from software engineering prep, and algorithm practice is still part of many technical loops. In our own 2026 data, 19% of data scientist interview reports mentioned LeetCode-style questions, compared with 52% for software engineers (full data in our report on AI and LeetCode in 2026 interviews). So for data scientists, algorithms are a slice of the loop, not the whole loop.

Our side-by-side of Interview Query and LeetCode walks through content, features and community. If you are choosing between two algorithm platforms, we also compared AlgoExpert and LeetCode for data science candidates. Both articles include prices from when they were written; check current prices on each site.

Choose LeetCode-style practice if you are interviewing for software engineering or an ML engineering loop with dedicated data structures rounds. Choose a data-specific platform if your loop is mostly SQL, metrics, experiments and modeling. Many ML engineers need both. Our data structures and algorithms learning path covers the algorithm material that data loops actually use.

StrataScratch focuses on coding practice for data science, ML and AI engineering roles. On its pricing page (November 11, 2026) it lists a free plan with 75+ coding questions and paid plans at $24 a month, $139 a year or $289 lifetime, with 1,000+ coding questions, 500+ ML, statistics and system design questions, 60+ interview loops by company and role, 50+ data projects, and AI mock interviews on paid plans. Its free SQL and Python learning paths are a good no-cost starting point.

Our in-depth comparison of Interview Query and StrataScratch covers how each one structures practice. The short version: StrataScratch is strong for hands-on coding volume; Interview Query adds more product case, experimentation and behavioral material, company guides and coaching. If SQL volume is your bottleneck, either works.

DataLemur's site says it was made by Nick Singh, author of the book Ace the Data Science Interview, and that the book has 201 questions. The site offers SQL, statistics, ML and Python interview questions tagged by company, plus a free 9-day data interview crash course and a free SQL tutorial. DataLemur also sells a Premium plan; its price was not visible without signing in when we checked.

Our comparison of Interview Query and DataLemur goes deeper on question style. DataLemur is a focused, low-friction place to sharpen SQL. If your loop adds product cases, experiments, take-homes and behavioral rounds, you will need more than SQL drills.

Exponent now operates as Aced; tryexponent.com redirects to aced.io, and the site carries a "formerly Exponent" label. On its pricing page, Aced lists a free Basic tier (sample lessons, sample questions, 5 peer mocks a month), a Pro tier (all courses, video and written solutions, company-specific guides, real interview experiences, unlimited peer mocks, the full question database) and an Expert AI tier that adds AI mock interviews, AI feedback and an AI coach. Its course catalog spans product management, software engineering, system design, data science, data engineering, ML and several business roles.

Our comparison of Interview Query and Exponent uses the Exponent name and covers the difference in approach: Aced leads with video courses across many roles, while Interview Query leads with hands-on practice for data roles. If you are a PM or switching between PM and analytics, Aced's breadth may suit you. If you are going deep on one data role, a data-specific question bank will give you more reps per hour.

DataInterview is another course-and-coaching option for data candidates. We did not re-verify its plans today, so see our comparison of Interview Query and DataInterview for how the two differ in format, and confirm prices on its site.

SQL is in almost every data loop, so it deserves its own tool choice. Our guide to the best SQL practice websites sorts options by goal: interview prep, beginner tutorials, real-world datasets and testing queries. If drills feel like a chore, these SQL games make the basics stick before you move to timed interview questions.

Two concept pieces help with the questions interviewers ask around SQL. Read when to reach for Python and when SQL is the better tool, and, if your loop has a system design round, how to choose between SQLite and PostgreSQL with interview-style examples. When you are ready for real questions, the SQL interview learning path runs from joins to window functions.

AI interviewers are useful for volume: you can run a session at midnight, repeat the same case three times and see how your answer changes. They are weaker at reading nuance, and they do not replace a human who has run real loops. We compared AI and human mock interviews on what each improves, and ranked six AI interviewer platforms for tech interviews. Interview Query is one of the six, so read that ranking knowing who wrote it.

If you are using a general chatbot as your interviewer, our guide to practicing interview answers with AI shows how to build a real workflow instead of just talking at your phone. Our own AI mock interview runs product case, statistics, SQL, Python, ML and behavioral rounds and scores the transcript; your first session is free with an account.

This matters more each year. In our 2026 data, reports describing an AI interviewer as part of a real hiring process rose from 0.5 per 1,000 interview reports in Q1 2024 to 7.0 per 1,000 in Q3 2026. Practicing with one means the real AI screen will not be your first.

A coach is the most expensive option and the one most likely to catch the thing you cannot see in yourself: rambling answers, weak structure, missing trade-offs. Our explainer on what data science coaching is and when it pays off covers how sessions work. If you want a longer relationship rather than paid sessions, read how to find a data science mentor.

You can meet two of our coaches in profile interviews: Jitesh, a data engineering and analytics coach who worked at Amazon and AWS, and Sundeep, an AI and NLP coach who was a research scientist at Amazon Alexa AI. Book a 1:1 coaching session when you have an onsite on the calendar and want a mock with feedback from someone who has interviewed candidates.

Books give you the reasoning behind the answers, which platforms often skip. Start with our list of the best data science interview books, which reviews each one's strengths and gaps. Then add one book for your track:

• Data engineers: our data engineering book list includes Designing Data-Intensive Applications, Fundamentals of Data Engineering and The Data Warehouse Toolkit.
• Product analysts and product data scientists: our product analytics reading list includes Lean Analytics, Experimentation Works and Data Science for Business.

Pair reading with practice. A chapter on experimentation is worth more after you have tried to answer three A/B testing questions and gotten one wrong.

You can get far for free. Our guide to free tools every data science major should know covers notebooks, datasets and learning platforms, and our list of data analyst tools worth learning covers visualization, BI and querying tools that show up in job descriptions. For portfolio projects, Hugging Face Datasets gives you thousands of ready-to-load datasets with a few lines of Python.

If you are moving toward AI engineering, compare generative AI courses and certifications, from vendor certs to university programs, before you pay for one. A certificate helps you get past a filter; it does not replace practice for the interview itself.

Data scientist (product or analytics). Expect SQL, statistics, A/B testing, product metrics and a product case. Use a data-specific bank for those rounds, an AI or peer mock for the product case, and an interview book for statistics. The statistics and A/B testing learning path and the product metrics path map directly to those rounds.

Data analyst or business analyst. SQL dominates, then metrics, Excel or BI, and behavioral. DataLemur, StrataScratch or Interview Query all work for SQL; add the data analytics interview path and practice explaining a dashboard out loud.

Data engineer. SQL, data modeling, pipelines, Python and system design. Use the data engineering interview path, one book on data systems, and algorithm practice if the company runs a coding round.

ML or AI engineer. Coding and algorithms, ML theory, ML system design, and more and more LLM questions. In our 2026 first-hand reports, 47% of ML and AI engineer interviews mentioned AI, LLMs or generative AI. Combine LeetCode-style practice with the modeling and machine learning path and prepare one project you can defend in depth.

• SQL screen: timed SQL practice on any of the SQL platforms above, then explain your query out loud.
• Statistics and probability: an interview book plus the probability path.
• Product case and metrics: written practice with feedback (our case study checker or a coach) and spoken practice in an AI or peer mock.
• Machine learning: concept questions plus one end-to-end project story.
• Coding and algorithms: LeetCode-style problems or the Python interview path.
• Take-home: practice on a real assignment. Our take-home challenges include exercises from companies such as Airbnb, Uber, Lyft and DoorDash.
• Behavioral: write 6 to 8 stories, then say them out loud to an AI interviewer or a friend until they fit in two minutes.
• Company-specific loop: read the company interview guide for the process, rounds and recently reported questions before you plan anything else.

2-week plan (interview already scheduled)

• Days 1 to 2: read the company guide and list the rounds. Take one assessment to find your weakest area.
• Days 3 to 9: 5 to 8 questions a day in your two weakest rounds, timed. Use free prompts first; pay only if you need solutions and walkthroughs.
• Days 10 to 12: two AI mock interviews (product case and behavioral) and one human mock if you can get one.
• Days 13 to 14: review misses, rehearse your three best stories, rest.

4-week plan

• Week 1: SQL and Python fundamentals with a free learning path or tutorial; read the first half of one interview book.
• Week 2: statistics, probability and A/B testing; 20 or more questions.
• Week 3: product cases and ML; one take-home under real time limits.
• Week 4: company guide, 3 to 4 mock interviews, behavioral stories, and review of every question you missed.

8-week plan (career switch or first data job)

• Weeks 1 to 2: free foundations (SQL, Python, statistics), plus one portfolio dataset.
• Weeks 3 to 4: a structured learning path for your target role.
• Weeks 5 to 6: daily timed questions mixed across rounds, two take-homes, one domain book.
• Week 7: mock interviews every other day, AI and human.
• Week 8: company-specific prep for your active applications.

Universities follow a similar pattern at scale. Purdue's MS in Business Analytics built Interview Query into its curriculum and reported 100% placement within six months for its 2023 class. UCLA Anderson's MSBA ran onboarding webinars and saw 80% of students start practicing in the first week. A professor at Loyola Marymount used our take-home challenges in SQL and business analytics classes, and Duke's MQM program focused on the first five minutes of the interview for international students. These are partner programs and their results reflect many factors beyond any one tool.

Is LeetCode enough for data science interviews?

Usually not. LeetCode covers coding and algorithms, which are part of some data loops, but most data science interviews also test SQL, statistics, experiments, product sense and behavioral answers. In our 2026 data, 19% of data scientist interview reports mentioned LeetCode-style questions.

What is the best free resource for data science interview prep?

Free SQL tutorials and learning paths (StrataScratch and DataLemur both offer free ones), Interview Query's free question prompts and first AI mock interview, and one library book. Free resources cover fundamentals well; paid plans mostly add worked solutions, structure and mock interviews.

Is Interview Query worth paying for?

It is worth it if your target loop includes product cases, experiments, ML and behavioral rounds and you want solutions, company guides and AI mocks in one place. If you only need SQL reps, a cheaper SQL-focused tool may be enough.

How long does it take to prepare for a data science interview?

Two weeks if you already work in data and have an interview scheduled, four weeks for a typical job change, and eight weeks or more for a first data job or a career switch.

Are AI mock interviews worth it?

Yes for repetition and getting used to speaking your reasoning, and because AI screens are now part of real hiring processes. Pair them with at least one human mock before an onsite.

What happened to Exponent?

Exponent now operates as Aced. tryexponent.com redirects to aced.io, which describes itself as "formerly Exponent."

Do I need a book if I use a prep platform?

One good interview book helps with the reasoning behind statistics and product questions. Most candidates do well with one interview book and one book for their track.

Written by Jay Feng, founder and CEO of Interview Query. Interview Query product facts were taken from interviewquery.com and its public API on November 11, 2026; list prices can differ during sales or pricing tests. Competitor facts were taken from each competitor's own website on November 11, 2026; LeetCode's site could not be checked, so no LeetCode prices or counts appear here. Interview data figures come from our published reports, linked where cited. We update this page when a platform changes its plans; if you see something out of date, email support@interviewquery.com.

Last updated: November 11, 2026