Whether you are stepping into your first role or preparing for a senior transition, technical interview loops for data roles demand a mix of statistical rigor, business intuition, and tool fluency. This guide outlines the top concepts, processes, and interview questions you will face, updated for today's modern data landscape. Part 1: Core Concepts & Statistical Foundations 1. What is an outlier, and how do you handle it? An outlier is a data point that differs significantly from the overall pattern of a dataset. Outliers can stem from entry errors, measurement flaws, or genuine high-variance events. Univariate Outliers: Deviate extremely within a single feature (e.g., an age recorded as 150). Typically identified via Z-Scores ( $\vert{}Z\vert{} > 3$ ) or the Interquartile Range Rule ( $1.5 \times \text{IQR}$ ). Multivariate Outliers: Deviate when multiple variables are combined (e.g., a 12-year-old with a PhD). Detected using distance metrics like Mahalanobis Distance...
The Next-Gen Guide to AI Integrations, Jasper BI, and Data Warehousing.