CompTIA Data+ Course Notes

Know how data is structured, where it comes from, where it lives, which tools fit the task, and the basic AI terms CompTIA expects. Structured data fits rows/columns; JSON is semi-structured and can nest values. Data lakes favor raw/diverse data, while warehouses favor curated/structured data. Match cloud and storage choices to control, access, scalability, and workload needs. Pandas is a Python data-manipulation library. AI-generated output must be validated.

• Treating null as zero or blank.

• Translate the scenario into a category first: structure, source, infrastructure, tool, or AI.

This domain is about getting data, spotting problems, and preparing it correctly. JOIN combines related columns/records by a condition; UNION appends compatible result sets. ETL transforms before loading; ELT loads first and transforms in the target platform. Investigate missing values, duplicates, redundancy, and outliers before changing them. Know what each transformation does: parse, convert, standardize, merge, append, explode, impute, scale, bin, cluster, or delete.

• Using JOIN when the task is to append rows.

• Ask: combine columns/related records, or append rows/results?

This is the heaviest domain. Match the analysis and communication method to the business purpose. Tailor language and detail to the audience, keep sensitive data protected, and tie KPIs to business objectives. Descriptive summarizes what happened; inferential generalizes from samples; predictive forecasts; prescriptive recommends actions. Median is more resistant to outliers than mean. For troubleshooting, verify connectivity and credentials, inspect logs, confirm the authoritative source, reproduce the issue, and isolate the defect.

• Using technical jargon for a non-technical audience.

• Read the desired outcome verb: summarize, infer, predict, or recommend.

Pick visuals based on the relationship you need to show, then choose delivery based on audience and freshness needs. Bar charts compare categories; line charts show change over time; scatter plots show association/correlation; histograms show distributions; maps show geography; pivot tables summarize groups. Snapshot/static delivery preserves a point-in-time view; real-time emphasizes freshness; recurring supports routine monitoring; ad hoc answers one-off questions. When reports fail, check the source and schema first, then transformations/calculations, refresh behavior, filters, and rendering.

• Inferring causation from correlation.

• Choose the visual from the relationship, not from personal preference.

Know who owns the truth, how data moved, what rules apply, how it is protected, and how quality is tested. Source of truth is the authoritative value; lineage traces origin and transformations; versioning tracks changes; snapshots preserve states; refresh intervals control freshness. Compliance depends on classification and jurisdiction. Privacy concerns rights and appropriate use; protection concerns safeguards. Classification drives controls such as RBAC, encryption, retention, and disposal. For QA: requirement tests check specified behavior, unit tests check transformation logic, UAT checks user needs, stress tests check performance under load, and monitoring detects drift or quality changes.

• Confusing lineage with metadata.

• Governance questions usually start with classification and authority.