Cleaning, Transformation and Validation
This topic covers deduplication, missing values, standardization, normalization, joins, calculated fields, validation rules, data dictionaries, and preparation documentation.
How to study for CompTIA Data+
Treat each Data+ item as an analytics decision: identify the source, quality issue, transformation, statistic, visual, audience, and control.
Core concepts
Concept 1
Cleaning should correct or flag quality problems without hiding decisions or changing business meaning.
Exam cue: Document cleaning decisions and preserve source meaning.
Concept 2
Transformation prepares data for analysis by standardizing units, formats, categories, joins, and derived fields.
Exam cue: Standardize units, formats, labels, and keys before joining.
Concept 3
Validation confirms prepared data meets rules, expected totals, referential relationships, and business definitions.
Exam cue: Validate totals, ranges, relationships, and business rules.
Risk pitfalls and guardrails
Deleting missing values without checking why they are missing.
Guardrail: Avoid answers that skip profiling, overclaim causation, hide limitations, choose misleading visuals, or expose sensitive data.
Joining tables on a nonunique descriptive field.
Guardrail: Avoid answers that skip profiling, overclaim causation, hide limitations, choose misleading visuals, or expose sensitive data.
Changing category labels without updating the data dictionary.
Guardrail: Avoid answers that skip profiling, overclaim causation, hide limitations, choose misleading visuals, or expose sensitive data.
Memory anchors
Deduplication
Deduplication removes or flags repeated records based on defined matching rules.
Missing Value
A missing value should be handled according to cause, impact, and analysis method.
Standardization
Standardization makes formats, units, labels, and codes consistent.
Normalization
Normalization can reduce redundancy in databases or scale variables for analysis, depending on context.
Join Key
A join key connects records across datasets and should be unique or intentionally repeated.
Calculated Field
A calculated field derives a new value from existing data.
Validation Rule
A validation rule checks whether data meets expected format, range, or business logic.
Referential Integrity
Referential integrity keeps relationships between tables valid.
Data Dictionary
A data dictionary defines fields, types, meanings, allowed values, and ownership.
Reconciliation
Reconciliation compares prepared outputs to source totals or trusted benchmarks.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
A CRM export contains exact repeated rows created by a failed reload. Which action best addresses the requirement? Use only the facts stated.
Income is missing mostly for respondents who selected 'prefer not to answer.' How should the analyst proceed? Use only the facts stated.
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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