Data Science interview questions and quick answers
Statistics, pandas, cleaning data and exploratory analysis for analyst and data science roles.
Review 989 questions and short answers on Data Science. Start with real examples below, then search, practise and follow links to full explanations.
Data Science: questions worth revising first
These answers come from AI Tapri's existing question bank and published interview guides. Open each answer and read the linked in-depth guide for examples and context.
01How do you decide whether an A/B test result on conversion rate is significant?
Compare the two conversion rates with a two-proportion z-test: z is the difference in rates divided by the pooled standard error. A p-value below your preset alpha, with a practically useful lift, supports shipping the change.
Read the full explanation02How does Bayes theorem work? Explain with a medical test example.
Bayes theorem updates a probability using new evidence: P(A|B) = P(B|A) x P(A) / P(B). It shows that a positive result from an accurate test can still mean a low chance of disease when the disease is rare.
Read the full explanation03When do you use the binomial distribution?
Use it to count successes in a fixed number of independent trials, each with the same probability of success. The probability of exactly k successes in n trials is C(n,k) p^k (1-p)^(n-k).
Read the full explanation04What is the birthday paradox?
In a group of just 23 people, the chance that two share a birthday is about 51%. It feels surprising because the number of pairs grows fast: 23 people form 253 pairs, each a chance for a match.
Read the full explanation05What is the Central Limit Theorem, and why does it matter?
The Central Limit Theorem says that the distribution of sample means approaches a normal distribution as the sample size grows, whatever the shape of the original data (given finite variance). That is why normal-based tests and confidence intervals work on messy real data.
Read the full explanation06How does the chi-square test of independence work?
It compares observed counts in a contingency table with the counts expected if the two categorical variables were independent. The statistic is the sum of (observed - expected)^2 / expected; a large value means the variables are related.
Read the full explanation07What is data science?
Extract insights from data. Statistics, programming, domain knowledge. Answer business questions.
08Explain machine learning.
Algorithms learn patterns from data. Supervised (labeled), unsupervised (unlabeled), reinforcement learning.
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Data Science quick answers
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