Explore Dataset to Assess Quality and Choose Visualizations
Overview
This question evaluates a candidate's ability to profile unfamiliar datasets, assess data quality (completeness, correctness, consistency, and bias), and select visualizations that communicate key findings to stakeholders.
Amazon
Jul 12, 2025, 6:59 PM
Data Scientist
Onsite
Analytics & Experimentation
21
0
Understanding a New Dataset: Profiling, Data Quality, and Visualization Plan
Scenario
You receive a new, unfamiliar dataset and must quickly generate insights and visualizations for business stakeholders on a tight timeline.
Task
Walk through the end-to-end steps you would take to:
- Understand what the dataset looks like (shape, schema, granularity, and contents).
- Assess data quality (completeness, correctness, consistency, and potential biases).
- Decide which visualizations to build to communicate key findings aligned with business goals.
Your answer should cover:
- Data profiling and schema understanding.
- Missing-value checks and other quality checks (duplicates, ranges, keys, outliers).
- Univariate and multivariate exploratory data analysis (EDA).
- Choosing chart types based on variable types (numeric, categorical, time) and business questions.
- Prioritizing visuals for a stakeholder readout and noting caveats.
- If applicable, guardrails for experimentation data (randomization checks, sample ratio mismatch, event ordering).
Solution
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