Exploratory Data Analysis and Hypothesis Generation Questions

Skills and processes for quickly understanding a dataset, identifying data quality issues, and turning initial observations into testable business hypotheses. Candidates should demonstrate techniques for profiling data, summarizing distributions, detecting outliers and missing values, exploring time series and cohort patterns, and visualizing key relationships. Discussion should cover practical tools and workflows for rapid exploration, how to document findings and assumptions, and how to prioritize hypotheses by business impact and data feasibility. Interviewers assess the candidate s ability to propose concrete next steps and simple tests to validate hypotheses and to surface instrumentation or data collection needs.