Data Science Interview Course

Data science interviews test a mix of statistics, programming, SQL, machine learning, and business judgment. Candidates are asked to explain concepts, write code, interpret metrics, and solve open-ended case studies. A strong study path starts with statistics and probability, then moves to Python and pandas, SQL, machine learning, and finally product metrics and A/B testing. You should be able to define p-values and confidence intervals, write pandas operations, join SQL tables, explain precision and recall, and design a simple experiment. Interviewers care about clear reasoning, correct terminology, and practical tradeoffs. This course organizes those topics into five lessons and links to a practice bank with 60 original questions to help you review before interviews.

Statistics questions check whether you understand uncertainty and data summaries. A p-value is the probability of observing data at least as extreme as the sample when the null hypothesis is true; it is not the probability that a hypothesis is true. Confidence intervals give a range likely to contain a parameter at a chosen confidence level. Standard deviation measures spread around the mean, and the median is more robust to outliers than the mean. Correlation measures the strength and direction of a linear relationship but does not prove causation. Common tests include two-sample t-tests for means, chi-square tests for categorical associations, and ANOVA for three or more groups. The Poisson distribution models event counts in a fixed interval, and Bayes theorem updates probabilities with evidence. Interviewers want clear definitions and examples, not memorized formulas.

Data science interviews often include live coding with pandas and SQL. In pandas, read_csv loads data, head shows the first rows, describe gives summary statistics, dropna removes missing values, and fillna replaces them. groupby splits data for aggregation, merge joins DataFrames like SQL, value_counts counts unique values, and apply runs a function across elements. NumPy provides arrays and Matplotlib creates plots. In SQL, WHERE filters rows, GROUP BY groups them, HAVING filters groups, ORDER BY sorts, and LIMIT restricts rows. INNER JOIN keeps matches, LEFT JOIN keeps all left rows, and DISTINCT removes duplicates. Aggregate functions include COUNT, AVG, SUM, MIN, and MAX. Practice writing these operations fluently because interviewers often ask for the simplest correct answer.

Machine learning questions focus on supervised versus unsupervised learning, model evaluation, and common algorithms. Supervised learning uses labeled data for classification or regression, while clustering is unsupervised. Logistic regression is common for binary classification, linear regression predicts continuous values, and K-means finds clusters. Overfitting happens when a model learns noise, and techniques such as limiting tree depth, pruning, and regularization help. Cross-validation evaluates models across multiple splits for a more reliable estimate. Precision is true positives divided by predicted positives, recall is true positives divided by actual positives, and F1 balances both. Accuracy can be misleading for imbalanced data, so F1, precision, and recall are often preferred. Interviewers expect you to connect algorithms to business questions and explain tradeoffs clearly.

Product case studies ask you to define the problem, choose metrics, design an experiment, and explain what you would do with the results. A metric is a measurable value such as conversion rate, retention, churn, or revenue per user. Conversion rate is conversions divided by visitors, retention is the share of users who return, and churn is users who stop using the product. Cohort analysis tracks groups over time. A/B testing randomly assigns users to a control and treatment group to measure the effect of a change. The null hypothesis assumes no effect, and a low p-value suggests the result is unlikely due to chance. Interviewers want structured thinking: clarify the goal, define the population, choose primary and guardrail metrics, estimate sample size, and decide next steps. Practice explaining why a metric moved and what you would investigate next.