How we compare
Real company interview questions, genuine business briefs that become portfolio work, and a timed interview you have to talk your way through. Everyone else sells you one piece of that.
Real business problems, not puzzles
Every question is a problem a company actually screens on, with the business context intact. You practice the work, not a riddle with a clever trick in it.
Portfolio work that looks like the job
Data Labs by Data Career School hands you a company scenario, messy data, published grading criteria and a notebook to solve it in. You ship a repository a hiring manager can open, and a project page in our public gallery under your own name — not another notebook on a famous dataset.
You have to defend it out loud
A timed spoken interview reads the code you ran and the result it returned, then asks why. Submitting starts the questions rather than ending them, and you finish with a hiring scorecard.
| Platform | What it is | From |
|---|---|---|
| Data Career School | Complete practice platform: company questions, business briefs with a public project gallery, and spoken mock interviews | From $10/mo |
| StrataScratch | Question library | ~$19/mo |
| DataLemur | SQL question bank | ~$15/mo |
| Interview Query | Question library with coaching add-ons | ~$79/mo |
| Maven Analytics | Course platform for BI tools | ~$49/mo |
| DataCamp | Course platform for learning a tool | ~$49/mo |
| Kaggle | Open dataset repository and competitions | Free |
| interviewing.io | Human mock interview marketplace | Per session |
Other platforms' pricing and descriptions are their own published figures as of October 2026 and may change. Verify current details with each provider.
We give you a business brief. Kaggle gives you a file.
Kaggle is an open repository — anyone can upload anything — and the datasets everybody reaches for arrive already cleaned. That is the problem. The hard, paid part of a data job is deciding what the question is, finding the data and working out what in it can be trusted. If that was done before you opened the file, your finished notebook does not demonstrate any of it.
There is no business question in a set of columns either, so projects end at an accuracy score instead of a recommendation. And because the same famous datasets circulate endlessly, a technically good project still leaves the reviewer unable to tell you apart from the last forty applicants.
Data Labs by Data Career School is built the other way round. Every lab is a brief modeled on a real company situation: the scenario, the business question, the task, the deliverable and the grading criteria, all stated before you start. The data is raw and imperfect on purpose. You finish with a repository and a recommendation you can defend in a room — which is what a hiring manager asked to see.
You can write the whole thing here too. Every brief comes with a Python notebook that has the project data beside it, runs in your browser, and saves as you work, so you pick up tomorrow where you stopped today. pandas, numpy and matplotlib, nothing to install, no environment to configure, and no compute quota to run out of.
Your finished project then gets a page of its own in the Data Career School project gallery, credited to you, where anyone — including a recruiter you send the link to — can read the notebook, the code and the write-up, upvote it, ask how you approached it, or copy the notebook and run their own version. Work is surfaced by what people found useful and by editorial picks, never by a leaderboard position on one dataset. The notebooks, the gallery, publishing, copying, upvoting and commenting are free and stay free. The part you pay for is the part that gets you hired.
One is a library of questions. The other is practice for the interview.
StrataScratch is a question library with AI-assisted practice, from about $19 a month. You pick a question, write an answer, check it.
Here, solving it is the start. You say your approach out loud before you write it and the interviewer answers you. When you submit, it reads the code you actually ran and the result it produced and asks about that specifically — why that join, what breaks on an empty table, what it costs at a billion rows. The session continues past the answer, because that is where real screens are won and lost. You leave with a scorecard that separates correctness from communication, so you can see which of the two is costing you offers instead of guessing.
And the practice is only one third of it: the portfolio briefs and the spoken interview sit on the same subscription, from $10 a month.
A question bank cannot hear you.
DataLemur is a SQL and data science question bank built around an interview book, from about $15 a month. It is written practice, on paper terms.
Nobody loses a data screen because they cannot type a join. They lose it in the ten seconds of silence while they think, on the follow-up after the right answer, and on being asked to walk through their own query and realizing they chose it by feel. For roughly the same money, this platform rehearses exactly that — out loud, on a clock, with a scorecard at the end — and gives you the company questions and the portfolio briefs alongside it.
Eight times the price for the part that matters least.
Interview Query is a large data science library with course content, company guides and coaching add-ons, at about $79 a month.
Breadth is the easy part of this market to buy and the hardest to use. You do not need another thousand questions; you need to be unshakeable on the two hundred patterns that actually recur, and able to talk through any of them while somebody interrupts. Data Career School starts at $10 a month and spends all of it on that: business-grade questions, portfolio briefs with grading criteria, and a spoken interview that makes you defend your answer.
They teach the tool. We get you hired with it.
Maven Analytics and DataCamp are course platforms, both around $49 a month. Maven leans on Excel, Power BI and Tableau with dashboard projects; DataCamp is a large library of guided lessons for picking up a tool from nothing. Both are reasonable places to learn syntax.
Neither is an interview. Courses are watched; interviews are survived. This platform assumes you already have some SQL or Python and is built entirely to convert it into offers — real company questions graded instantly, business briefs that become portfolio work, and a timed interview that interrogates what you wrote. At a fifth of the price.
Repetition is what removes the shake in your voice.
A live human gives you the highest realism available, and a session or two before a final round is worth having. The limits are obvious: cost per session, and a calendar.
Composure is not built in two sessions. It is built in twenty, and twenty is only possible when an interview is available the moment you want one and costs a fraction of a human hour. Every one here ends in the same structured scorecard, so you can watch communication move from two to four across a month — something no scattered set of human sessions will ever show you.
A general assistant wants you to feel good. An interviewer does not.
Ask a chatbot to interview you and it will discuss the question, praise your reasoning, and hand over the answer the moment you ask. It does not run your code against real tables, does not hold you to a clock, and does not grade you against the same rubric twice, so you cannot tell whether you are improving.
This is an adversarial format with a time limit and a scoring sheet: it pushes back once before it gives you anything, it keeps asking after you submit, and it scores every session the same way. The discipline is the product.
Not on its own. Kaggle is an open repository, so anyone can upload anything, and the popular datasets arrive already cleaned — which means your project never shows you can handle raw data, the part the job is actually about. A dataset is also not a business question, so projects end at an accuracy score rather than a recommendation, and reviewers have seen the same famous datasets hundreds of times. Data Labs by Data Career School gives you the opposite: a real company scenario, a stated business question, deliberately imperfect data, a defined deliverable and published grading criteria. You finish with work that looks like the job rather than like a tutorial.
Data Labs by Data Career School. For a portfolio you intend to put in front of employers you want briefs rather than datasets: a stated business problem, messy source data, a defined deliverable and criteria you are judged against before you start. Every lab is modeled on a real company situation and ends in a GitHub repository you own and can defend in an interview. You also get what people actually use Kaggle for — public work, upvotes, discussion, all free — in our project gallery, with a business problem behind every project and a page a recruiter can read. Kaggle remains the place to go for competitions and modeling practice.
You can do the whole project here. Every Data Labs brief by Data Career School comes with a Python notebook that has the project data beside it: write pandas, plot charts, run it, and come back tomorrow to find it exactly where you left it. There is nothing to install, no environment to configure and no compute quota to run out of, because it runs in your own browser rather than on a server we ration. Publish it and it becomes part of your project page in the gallery, where anyone can read it or copy it and run their own version. Free, and it stays free.
In the Data Career School project gallery. Every project there answers a stated business brief rather than a dataset somebody uploaded, and each one shows its author, the code, their write-up and the discussion around it. It is free to browse and needs no account, which makes it the fastest way to see the standard before you build your own — the thing most portfolio advice leaves you guessing at. Publishing your own project, upvoting and commenting are free too.
Data Career School, if you want to practice the way the job actually works. It is the only place that puts all three parts together: real company interview questions you run and are graded on, genuine business briefs that become portfolio work, and a timed spoken mock interview that challenges what you wrote and scores you on it. Question banks cover one of those three. Course platforms cover none of them. Being rejected from a data role is rarely about not having seen enough questions — it is about not being able to defend the one you were asked.
Those are question libraries. This is practice for the job. You talk through your approach out loud, the interviewer answers you, and when you submit it reads the code you actually executed and the result it returned and starts asking about that — why that join, what happens on an empty table, what it costs at a billion rows. Submitting begins the follow-up round rather than ending the session, and you finish with a hiring-style scorecard that separates correctness from communication. Communication is graded on technical substance rather than English fluency. And it starts at $10 a month against roughly $15 to $79 elsewhere.
No, and past a point it is the wrong thing to optimize for. Some platforms publish over a thousand questions. Nobody is rejected from a data role for having solved four hundred instead of a thousand — they are rejected for not being able to explain the one they were asked, out loud, while somebody interrupts them. Every question here is a real business problem drawn from a company screen, and every one of them can be taken into a spoken mock interview where you have to defend it. Depth of practice beats length of catalogue.
They are better at different things. A human gives you the highest realism and judgment, and nothing fully replaces that before a final round. An AI interview gives you repetition: you can sit ten of them in the time it takes to schedule one human session, and repetition under mild pressure is what removes hesitation. Most candidates who prepare well use both, with AI mocks for volume and a human session close to the real thing.
A general assistant will discuss a question with you and will usually be encouraging. It does not run your code against real tables, does not hold you to a clock, does not refuse to hand you the answer when you ask, and does not grade you against a consistent rubric across sessions so you can see movement. The discipline is the product.
It is worth it when it changes the outcome of a round you would otherwise fail. One additional offer, or a stronger negotiating position, is worth many years of a subscription. Premium starts at $10 a month with a 14-day money-back guarantee, which is a fraction of the cost of a single human mock interview.
The questions and most of the business briefs are free. Sit one spoken interview and you will know within thirty minutes whether the rest of this page is true.