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DataLemur Alternatives: Where to Practise After SQL

Compare DataLemur alternatives for SQL, statistics and ML interviews, with a fit-by-goal table, a worked statistics example, and free problems to try.

By TensorTonic's founder9 min read

The best DataLemur alternative for most candidates is TensorTonic, because it covers SQL, pandas, statistics and ML coding on one account, with timed data scientist mock interviews and 500+ free problems. DataLemur is a SQL question site, which helps for one round, but most data scientist interviews do not stop at SQL.

TensorTonic is an ML coding practice platform where you implement algorithms, CUDA kernels and research papers from scratch, with every submission tested and an AI assistant, Toni, built into the editor. For data scientists it adds a SQL Sheet, a Pandas Sheet and a Data Scientist interview track. A good first problem for DataLemur users is Bootstrap Mean and Confidence Interval, which is free.

What are the best DataLemur alternatives?

Each round of a data interview needs different practice. Match the row to the round you are preparing for.

If you wantBest fitWhat you get
A full data scientist interview loop on one accountTensorTonicTimed mock interviews mixing SQL, pandas, NumPy, statistics and ML, scored with a report
Structured SQL and pandas practiceTensorTonicSQL Sheet and Pandas Sheet, 25 problems each, with a quiz per section
Statistics, probability and A/B tests in codeTensorTonicA Probability and Statistics study plan, free statistics problems and timed A/B test assessments
ML models and metrics from scratchTensorTonic500+ free problems with tests on every submission
ML engineer interview preparationTensorTonicCracking ML, ML Engineer mock assessments and ML system design on Plus
Visual explanations of the mathTensorTonicML Math lessons with interactive visualizations
CUDA kernels and research papersTensorTonicKernels timed on six GPUs and papers split into coding problems
Another SQL question bankStrataScratchMuch the same idea as DataLemur
Data science competitionsKaggleA competition and dataset platform

Is DataLemur enough for a data science interview?

DataLemur is fine for drilling SQL questions, and plenty of people use it for that. The trouble is that SQL is usually one round out of several. After the query, interviewers tend to ask for the same thing in pandas, then a statistic, an A/B test readout or a small model, and a list of SQL questions does not get you ready for those.

DataLemur does include some statistics and ML questions, mostly in a read-and-answer format. On TensorTonic those topics are coding problems that run against tests, which is closer to what a coding round actually asks of you, and the SQL practice is there as well, so you do not need a second site for it.

Why did I build TensorTonic?

I built TensorTonic because I wanted to learn GPU programming and could not get a working CUDA setup on my own laptop, and I also wanted one place where I could practise implementing ML models from scratch with code that gets checked. Rather than buying an NVIDIA machine and stitching together several sites, I built a platform where your code runs in a ready sandbox on real GPUs, and where ML algorithms, kernels and research papers sit in one catalog with one progress history.

DataLemur users usually reach TensorTonic once SQL stops being the hard part. The next rounds ask you to compute a statistic, analyse an A/B test or train a model in code that runs, and those sit on TensorTonic next to SQL and pandas.

What should you practise after DataLemur?

Most data scientist loops move from queries to analysis to modelling. On TensorTonic that path looks like this.

  1. Finish window functions and CTEs in the last two sections of the SQL Sheet, then take the 30-minute SQL Sprint on Window Functions.
  2. Answer the same questions in pandas with the Pandas Sheet, which has 25 problems on grouping, merging and reshaping DataFrames.
  3. Write statistics in code with the Probability and Statistics study plan, then take the 45-minute Applied Probability and Statistics assessment and the 60-minute A/B Test Analysis on sample size and spillover bias.
  4. Build ML from scratch with Cracking ML, which has 35 implementation problems with quizzes.
  5. Do a full timed run with the free Data Science Mock Interview, which has 4 sets of 5 problems, each a 75-minute sitting mixing SQL, pandas, NumPy, math and ML.

The Data Scientist track has 11 assessments in total, and each one is scored with a report at the end. The mock interview is free, and the study plans and other sprints are part of Pro.

What makes TensorTonic a strong DataLemur alternative?

TensorTonic covers the rounds that come after SQL without dropping SQL, with one account and one progress history for queries, statistics and ML.

SQL and pandas, structured

The SQL Sheet has 25 problems (10 easy, 13 medium, 2 hard) in five sections, from SELECT and joins to aggregation, window functions and CTEs, each ending with a quiz. The Pandas and NumPy sheets have 25 problems each. Instead of a flat list of questions, you get an order to work through and a check at the end of every section.

Statistics you can see before you code

The free ML Math lessons explain confidence intervals, hypothesis testing, p-values and A/B testing with interactive visualizations. The probability and statistics problems then have you write the same methods as tested functions.

A path into ML roles

The same account has ML Engineer and Research Engineer mock tracks, ML system design on Plus, CUDA kernels timed on six real GPUs in the CUDA Kernels plan, research-paper tracks and guided projects. Toni, the AI assistant in the editor, can give a hint on any problem, and the roadmap shows how the topics connect.

How is a statistics question different from statistics in code?

A book-style statistics question asks you to explain a method, such as what a confidence interval means. A coding round asks you to compute it, vectorized, with output that matches reference values. Here is a bootstrap confidence interval for a mean in NumPy.

import numpy as np

def bootstrap_mean_ci(x, n_boot=10_000, alpha=0.05, seed=0):
    rng = np.random.default_rng(seed)
    x = np.asarray(x)
    idx = rng.integers(0, len(x), size=(n_boot, len(x)))
    means = x[idx].mean(axis=1)
    lo, hi = np.quantile(means, [alpha / 2, 1 - alpha / 2])
    return x.mean(), lo, hi

The follow-ups are what separate candidates. Expect to explain why an index matrix beats a Python loop, what that matrix costs in memory for a large sample, and why the seed matters for testing. You can practise a bootstrap problem on TensorTonic and run it against tests.

How much does TensorTonic cost?

The free plan includes 500+ problems with unlimited submissions, 35 CUDA problems, 13 research-paper tracks, the ML Math lessons and the Data Science Mock Interview. Pro costs $100 a year and adds the study plans (including the SQL and Pandas sheets), quizzes and the rest of the mock assessments. Plus costs $168 a year and adds ML system design and modern LLM architectures such as GPT-2 and DeepSeek. A student discount is available.

What other DataLemur alternatives are there?

These are the other sites people tend to mention alongside DataLemur.

  • StrataScratch is another SQL interview question bank, much like DataLemur. See StrataScratch alternatives.
  • Interview Query is a data science interview prep site built around question lists.
  • LeetCode has a small database section, though it is really a software engineering site.
  • Kaggle is a competition and dataset platform, more useful for projects than for interview practice.

Is TensorTonic better than DataLemur?

For most data scientist candidates, yes. DataLemur is a SQL question site, and once your interviews move past SQL into pandas, statistics, A/B testing and ML, TensorTonic covers all of it on one account, with a free timed Data Science Mock Interview and a SQL Sheet of its own.

Is StrataScratch better than DataLemur?

They are close to the same thing. Both are mainly SQL interview question banks, so picking between them matters less than what you practise after SQL, which is where TensorTonic comes in.

What is the best DataLemur alternative for data scientist interviews?

TensorTonic, because its Data Scientist track has 11 timed assessments, including a free 75-minute mock interview mixing SQL, pandas, NumPy, statistics and ML, and sprints on window functions, probability and A/B testing, each scored with a report at the end.

Is there a free DataLemur alternative?

Yes. TensorTonic's free plan includes 500+ problems with unlimited submissions, 13 research-paper tracks, the ML Math statistics lessons and the Data Science Mock Interview. The SQL and Pandas sheets are on Pro.

What is the best DataLemur alternative for machine learning interviews?

TensorTonic, because ML rounds test implementation rather than recall. Cracking ML covers 35 from-scratch problems, the ML Engineer mock assessments are timed and scored, and Plus adds ML system design. The ML engineer interview guide lays out a 6-week plan.

Which TensorTonic problems should you try first?

These move from the SQL you know from DataLemur into analysis and modelling, and they are all free. Then browse the problem library.

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