What does the Pandas `groupby()` method return?

A GroupBy object that can be aggregated with functions like sum(), mean(), or count().

Which NumPy function stacks arrays along a new axis?

np.stack() — it joins a sequence of arrays along a new axis.

What metric is most appropriate when class imbalance is present?

F1 score or AUC-ROC, which account for both precision and recall rather than raw accuracy.

What is the purpose of cross-validation in machine learning?

To estimate a model's generalization performance by training and evaluating on different data folds, reducing overfitting risk.

What does a high bias (underfitting) model indicate?

The model is too simple to capture the underlying pattern — poor performance on both training and test sets.

How many questions are in the data science practice test?

The full test (Hard mode) has 25 questions across 7 topics. Easy mode covers Python library basics; Hard mode includes ML theory and evaluation metrics.

What Python libraries do the questions cover?

Questions focus on NumPy, Pandas, and scikit-learn. Matplotlib is touched on briefly. The emphasis is on data manipulation and ML workflows, not visualization.

Is this practice test useful for data science job interviews?

Yes. Questions are modelled on what data scientists and ML engineers are commonly tested on during technical screening rounds.

Is it free? Do I need to sign up?

Completely free, no account needed. Pick a difficulty and start.

What do I get at the end?

An instant readiness score (0–100), pass likelihood, a topic-by-topic accuracy breakdown, and your exact weak areas to focus on before your interview.

Is this a good data scientist exam practice test?

Yes. The questions mirror the ML, statistics, and Python library topics that data scientist certification exams and technical screening rounds assess. Use your topic-by-topic score to find the gaps in your data scientist exam prep before test day.

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Readiness test · Free · No sign-up required

Free Data Science Practice Test 2026

Take this free data science practice test and find out if you are ready for certification or your next interview. 25 exam-style questions, instant readiness score, topic-by-topic accuracy breakdown — no sign-up required.

One question at a time, explained as you go · no sign-up

Instant resultsTopic breakdownWeak area detectionPass likelihood

What this test covers

This free data science practice test covers the core Python data science stack and ML fundamentals tested in data science certifications and ML engineering interviews. Questions span libraries, statistics, and model evaluation — the same topics examiners target. Use your difficulty rating and per-topic score to guide your study plan.

NumPy Fundamentals
Pandas & DataFrames
Statistics & Probability
Machine Learning Concepts
Model Evaluation & Metrics
Feature Engineering
Data Cleaning & Preprocessing

How it works

Pick your difficulty

Choose Easy, Medium, or Full test depending on where you are in your learning.

Answer questions

One question at a time. Click an answer and get instant feedback with a full explanation.

Get your score

See your readiness score (0–100), pass likelihood, and a topic-by-topic breakdown.

Frequently asked questions

How many questions are in the data science practice test?: The full test (Hard mode) has 25 questions across 7 topics. Easy mode covers Python library basics; Hard mode includes ML theory and evaluation metrics.
What Python libraries do the questions cover?: Questions focus on NumPy, Pandas, and scikit-learn. Matplotlib is touched on briefly. The emphasis is on data manipulation and ML workflows, not visualization.
Is this practice test useful for data science job interviews?: Yes. Questions are modelled on what data scientists and ML engineers are commonly tested on during technical screening rounds.
Is it free? Do I need to sign up?: Completely free, no account needed. Pick a difficulty and start.
What do I get at the end?: An instant readiness score (0–100), pass likelihood, a topic-by-topic accuracy breakdown, and your exact weak areas to focus on before your interview.
Is this a good data scientist exam practice test?: Yes. The questions mirror the ML, statistics, and Python library topics that data scientist certification exams and technical screening rounds assess. Use your topic-by-topic score to find the gaps in your data scientist exam prep before test day.