Exploratory Data Analysis is the systematic process of investigating and validating a dataset to understand its structure, content, and quality before modelling or reporting. Core activities include examining schema and data types, computing descriptive statistics such as counts, means, medians, standard deviations and quartiles, and measuring class balance and unique value counts. It covers distribution analysis, outlier detection, correlation and relationship exploration, and trend or seasonality checks for time series. Data validation and quality checks include identifying missing values, anomalies, inconsistent encodings, duplicates, and other data integrity issues. Practical techniques span SQL profiling and aggregation queries using GROUP BY, COUNT and DISTINCT; interactive data exploration with pandas and similar libraries; and visualization with histograms, box plots, scatter plots, heatmaps and time series charts to reveal patterns and issues. The process also includes feature summary and basic metric computation, sampling strategies, forming and documenting hypotheses, and recommending cleaning or transformation steps. Good Exploratory Data Analysis produces a clear record of findings, assumptions to validate, and next steps for cleaning, feature engineering, or modelling.
HardTechnical
0 practiced
You have three datasets representing customers from different systems with inconsistent entity identifiers and partial overlapping attributes. Describe a rigorous EDA approach to link these datasets: candidate matching rules, how to quantify linkage precision and recall using a labeled sample, and how to document assumptions and ambiguous matches for downstream analysts.
HardSystem Design
0 practiced
You are handed a 1TB event dataset partitioned by date in a data warehouse and need to produce an EDA summary (top-level metrics, distribution snapshots, and data-quality checks) within 24 hours. Outline an actionable plan: sampling strategy, representative SQL queries or pre-aggregation steps, compute/resource considerations, and validation to ensure rare events and seasonality are captured in your sample.
HardTechnical
0 practiced
Large-scale EDA shows event timestamps sometimes occur after server processing timestamps (event_time > processed_time). Propose a reproducible triage workflow to determine root cause (sensor clock drift, timezone misinterpretation, delayed ingestion), methods to quantify the scope of affected records, and actions to fix historical data and prevent recurrence including monitoring ideas.
MediumTechnical
0 practiced
You are given a product sales table (product_id, sale_date, price, quantity). Describe an end-to-end EDA plan to identify seasonality and best-selling products. Include the SQL queries or aggregations you would run, the visualizations you would produce (list specific plots), and statistical checks you would perform to confirm seasonality.
MediumTechnical
0 practiced
A date column in your dataset contains inconsistent string formats such as '2024-01-05', 'Jan 5, 2024', and '05/01/2024'. Outline a robust, reproducible approach in Python or SQL to normalize this column to a canonical timestamp, detect unparseable rows, and create a validation report capturing parsing failures and their possible causes.
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