Data Science Interview Questions: Stats, SQL and ML Basics
The data science interview topics asked most — statistics, SQL, machine learning basics, and case questions — with tips to prepare and explain your thinking clearly.
By ApnaWorker - reviewed by ApnaWorker Editorial Team - updated 2026-06-16T04:59:25.541+00:00
Data science interviews blend several skills — statistics, coding (usually SQL and Python), machine learning fundamentals, and the ability to turn data into clear, useful answers. For most roles you do not need to know everything, but you do need solid fundamentals and clear communication.
This guide walks through the topics that come up most, what each is testing, and how to prepare so you can explain your reasoning calmly under pressure.
Statistics and probability basics
Interviewers check that you understand the foundations behind the models, not just how to call a library. Expect questions on averages, distributions, and how to reason about uncertainty.
Be comfortable explaining mean vs median, variance, correlation vs causation, and the basic idea of a hypothesis test or a p-value in plain language. Explaining clearly matters as much as the formula.
- Mean, median, variance, and when each is useful.
- Correlation vs causation — and why the difference matters.
- The intuition behind hypothesis tests and p-values.
SQL and data manipulation
Almost every data role tests SQL because that is how you get the data in the first place. Practise until joins, grouping, and filtering feel natural.
Be ready to write queries that join tables, aggregate with GROUP BY, filter with WHERE/HAVING, and rank or deduplicate rows. Talk through your query as you build it.
- SELECT, WHERE, GROUP BY, HAVING, ORDER BY.
- Inner vs left joins and when to use each.
- Aggregations, counts, and finding duplicates or top-N rows.
Machine learning fundamentals
For most roles, depth of intuition beats naming many algorithms. Interviewers want to know you understand how models learn and how to tell whether they are any good.
Be ready to explain supervised vs unsupervised learning, overfitting and how to prevent it, the train/test split, and a few common metrics like accuracy, precision, and recall — and when each is misleading.
- Supervised vs unsupervised learning with examples.
- Overfitting, train/test split, and cross-validation idea.
- Metrics: accuracy, precision, recall — and their limits.
Case and business questions
Many interviews include an open question like "how would you measure if a feature is working?" These test whether you can turn a vague business problem into a measurable, data-driven plan.
Structure your answer: clarify the goal, choose a metric, consider the data you would need, and mention how you would check the result. Showing clear, practical thinking matters more than a perfect answer.
- Clarify the goal and the decision behind the question.
- Pick a sensible metric and the data you would need.
- Explain how you would validate your conclusion.
How to prepare and present yourself
Practise explaining your thinking out loud — interviewers value clear communication because data scientists must explain results to non-technical people. Have a project or two you can walk through end to end.
Be honest about what you know. Saying "I have not used that, but here is how I would approach it" lands far better than bluffing. Calm, structured, honest answers win data interviews.
- Practise explaining concepts simply, out loud.
- Prepare a project you can walk through end to end.
- Be honest about gaps and show how you would learn.
Frequently asked questions
What should I study most for a data science interview?
Statistics fundamentals, SQL, and machine learning basics (overfitting, train/test split, common metrics), plus the ability to turn a business question into a measurable plan. Clear communication ties it all together.
How important is SQL in data science interviews?
Very important — it is how you access data. Practise joins, GROUP BY, filtering, and finding top-N or duplicate rows until they feel natural, and talk through your query as you write it.
Do I need to memorise many machine learning algorithms?
No. Intuition beats memorisation. Be able to explain how models learn, what overfitting is, how you split data, and which metrics to trust for a given problem.
How do I answer open-ended case questions?
Structure it: clarify the goal, choose a metric, identify the data you would need, and say how you would validate the result. Clear, practical thinking matters more than a perfect answer.