Learn
The data cleaning curriculum
Module 1
Inspect before changing
Read the brief and data dictionary, profile the schema, state assumptions and list open questions.
Module 2
Import correctly
CSV quoting, headers, encoding, delimiters, explicit types and file provenance.
Module 3
Missingness
Null versus blank versus zero versus unknown; keep the reason and handle each on purpose.
Module 4
Text and Unicode
Trim explicit whitespace, normalize permitted forms, preserve names and diagnose invisible characters.
Module 5
Categories and mappings
Approved dictionaries, unmatched values, controlled vocabularies and reversible mappings.
Module 6
Numbers and money
Locale-aware parsing, exact decimals, units, separate currencies and explicit rounding.
Module 7
Dates and time
Date-only values, format ambiguity, offsets, time-zone context and invalid-date quarantine.
Module 8
Types and identifiers
Leading zeros, large IDs, booleans, nullable types and lossless serialization.
Module 9
Duplicates
Row grain, composite keys, deterministic survivors, provenance and legitimate repeats.
Module 10
Entity resolution
Candidate pairs, exact versus fuzzy evidence, false merges and human review.
Module 11
Validation rules
Required fields, ranges, allowed categories, cross-field logic, severity and exception reports.
Module 12
Joins
Referential integrity, null keys, cardinality checks, orphan rows and duplicated totals.
Module 13
Combining files
Schema union, required and optional fields, source lineage and rejected files.
Module 14
Reshaping
Pivot collisions, aggregation, unpivoting, and zeros versus absent observations.
Module 15
Outliers and context
Flag anomalies without deleting legitimate extremes or silently changing the population.
Module 16
Reproducible pipelines
Immutable raw data, idempotent steps, new batches, tests and schema drift.
Module 17
SQL cleaning
A declared dialect, CTEs, CASE, trimming, typed casts, window functions and validation queries.
Module 18
Python and pandas cleaning
Nullable dtypes, explicit parsing, masks, merge validation and reusable functions.
Module 19
Analysis readiness
Train/test leakage, fitted transforms, reconciliation, distribution shift and transparent reports.
Module 20
Job-ready delivery
End-to-end projects, trade-off explanations, handoffs, take-home assessments and interviews.