Choose Your Level
Pick the difficulty that matches where you are. You can come back and try a harder level later.
Clean a Messy CSV and Write an Insights Memo
Turn a realistic messy dataset into three insights a manager can act on.
Handling Missing Data in a Healthcare Dataset
Decide which imputation strategy to use for each column and defend every choice.
Deduplication at Scale
Design a fuzzy-matching deduplication pipeline for a CRM with 500K records.
Data Cleaning, Wrangling & Preprocessing
Real-world data is dirty, incomplete, and full of inconsistencies. Data analysts spend over 60% of their time wrangling data. Hiring managers value candidates who master deduplication, outlier detection, date format standardization, and automated data quality checks.
1. Raw vs Cleaned Data Audit Log
Audit report detailing initial data anomalies, missing value distributions, and data cleaning transformations.
2. Automated Data Cleaning Script
Python or SQL script automating deduplication, text stripping, date parsing, and type casting.
3. Cleaned Master Dataset & Spec
Cleaned dataset export alongside data dictionary defining field definitions and valid ranges.
Frequently Asked Questions (Data Cleaning & Insights)
How do you identify statistical outliers in a dataset?
Outliers are commonly identified using the Interquartile Range (IQR) method (values outside 1.5 * IQR) or Z-score thresholds (|Z| > 3).
Why is string normalization necessary during data cleaning?
Inconsistent casing or trailing whitespace creates fake distinct categories during GROUP BY operations.
What is data imputation?
Data imputation is the process of replacing missing data values with substituted estimates (e.g. median or k-NN nearest neighbor values).
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