What Is CompTIA Data+?

Overview

CompTIA Data+ is a CompTIA credential. Develop data analysis, visualization, governance, and mining skills to support data-driven decisions. This PathKey™ guide summarizes the domains, format, cost signals, eligibility, and renewal pattern so you can decide before you book. Accumentum is a CompTIA Authorized Training Partner and an Army CA and Air Force COOL Approved Vendor. CompTIA sets exam content, fees, and policies.

Accumentum is a CompTIA Authorized Training Partner and an Army CA and Air Force COOL Approved Vendor. CompTIA sets exam content, fees, and policies.

Who it is for

• Aspiring data analysts, business analysts, reporting specialists, operations analysts, and entry-level data professionals seeking the CompTIA Data+ certification through expert-led training.
• Professionals in project management, finance, HR, marketing, sales, operations, logistics, customer support, IT, or compliance roles who need to interpret data, prepare reports, and communicate measurable business insights.
• IT professionals, database support personnel, help desk technicians, system administrators, and cybersecurity professionals who want to strengthen their data literacy and analytics capabilities.

Exam domains

Weights follow the official outline.

1. 01

   Data Concepts, Environments, and the Analytics Lifecycle

   Build a practical foundation in data analytics by learning how organizations define business questions, collect information, manage data environments, analyze patterns, and convert findings into decisions, Understand common data roles, analytics workflows, data lifecycle stages, business requirements, key performance indicators, data ownership, source systems, and stakeholder expectations, and Compare structured, semi-structured, and unstructured data while learning how files, databases, cloud platforms, data warehouses, data lakes, APIs, and business applications support analytics work.

2. 02

   Data Structures, Types, Schemas, and Database Fundamentals

   Identify common data types, field formats, data structures, categorical and numerical variables, measures, dimensions, metadata, primary keys, foreign keys, and relationships between tables, Learn how relational databases, flat files, spreadsheets, JSON, XML, logs, CRM systems, ERP systems, cloud services, and business intelligence platforms store and expose data for analysis, and Understand schemas, normalization, denormalization, data models, data dictionaries, entity relationships, constraints, and the impact of database design on reporting accuracy and analytical performance.

3. 03

   Data Acquisition, Business Requirements, and Source Evaluation

   Translate business questions into data requirements by identifying the audience, purpose, scope, source systems, granularity, time horizon, metric definitions, and expected outputs, Learn acquisition methods including database queries, exports, imports, APIs, surveys, logs, third-party datasets, manual collection, automated pipelines, and recurring reporting feeds, and Evaluate sources for relevance, completeness, timeliness, accessibility, reliability, ownership, lineage, refresh cadence, sensitivity, and potential bias.

4. 04

   Data Preparation, Profiling, Cleansing, and Quality Review

   Prepare datasets for analysis by profiling values, identifying missing data, detecting duplicates, reviewing outliers, validating formats, standardizing categories, and confirming record counts, Learn how cleaning decisions affect accuracy, reproducibility, trust, and business interpretation when working with incomplete, inconsistent, noisy, or conflicting data, and Understand common data quality dimensions including accuracy, completeness, consistency, validity, uniqueness, integrity, timeliness, and fitness for purpose.

5. 05

   Data Transformation, Manipulation, Validation, and Analytical Readiness

   Transform data using filtering, sorting, aggregation, joins, calculated fields, conditional logic, formatting, normalization, pivoting, parsing, binning, and derived metrics, Understand how analysts prepare data for dashboards, reports, trend analysis, variance analysis, comparisons, statistical summaries, and stakeholder-ready deliverables, and Learn validation techniques such as reconciliation, spot checks, control totals, cross-source comparison, formula review, row count checks, and peer review.

6. 06

   Data Analysis, Statistics, Trends, and Analytical Methods

   Analyze data using descriptive statistics, distributions, measures of central tendency, variability, correlation, trends, comparisons, segmentation, time-series review, and performance metrics, Understand when to use techniques such as aggregation, ranking, grouping, filtering, drill-down, cohort review, regression awareness, hypothesis framing, and anomaly identification, and Learn how to avoid common analytical errors including confusing correlation with causation, ignoring sample limitations, overgeneralizing findings, misreading outliers, and using inappropriate metrics.

7. 07

   Interpretation, Business Insight, and Decision Support

   Convert analysis outputs into business insight by interpreting trends, patterns, anomalies, performance changes, operational drivers, and measurable outcomes, Learn how analysts connect metrics to business questions, evaluate confidence, explain assumptions, assess limitations, and recommend next steps based on evidence, and Develop decision-support skills for stakeholders who need to understand what changed, why it matters, what action should be considered, and how impact should be measured.

8. 08

   Visualization Design, Dashboards, and Report Development

   Select appropriate visualizations including tables, bar charts, line charts, scatter plots, histograms, heat maps, scorecards, geographic views, waterfall charts, and dashboards based on the analytical goal, Learn visualization best practices for clarity, labeling, scaling, color use, accessibility, chart selection, layout, filtering, interactivity, audience alignment, and avoiding misleading design choices, and Understand how dashboards should support performance monitoring, trend review, operational management, executive reporting, and recurring decision cycles.

9. 09

   Reporting, Data Storytelling, and Stakeholder Communication

   Develop reporting communication skills that help stakeholders understand the business question, methodology, key findings, limitations, risks, and recommended actions, Learn how to create executive summaries, recurring reports, analytical narratives, dashboard notes, metric definitions, findings documentation, and stakeholder-ready presentations, and Understand how audience needs change the level of detail, visualization type, language, structure, and delivery format used in a report or analytical briefing.

10. 10

    Data Governance, Quality, Privacy, Security, and Controls

    Understand data governance concepts including stewardship, ownership, access controls, data classification, lineage, retention, privacy, compliance, master data management, and data quality management, Learn how analysts support responsible data use by following policies, protecting sensitive data, documenting sources, validating metrics, and respecting regulatory and organizational requirements, and Explore controls that improve trust in data, including standard definitions, approved sources, audit trails, change management, reconciliation, documentation, and quality monitoring.

Exam format and cost

Questions

Confirm on the vendor site

Duration

Confirm on the vendor site

Format

Multiple choice and related item types (confirm on the vendor outline)

Passing score

675

Cost

Confirm the current U.S. voucher price on CompTIA's store before you buy (often listed in the mid-$300s to low-$400s USD in 2026)

Delivery

Confirm delivery options on the vendor site

Languages

Confirm available languages on the vendor site

Experience and eligibility

No strict prerequisites are required to enroll in CompTIA Data+. The program supports motivated beginners while also benefiting professionals who already work with reports, spreadsheets, dashboards, databases, or business metrics. CompTIA recommends 18 to 24 months of experience in a data analyst or similar job role, with exposure to databases, analytical tools, basic statistics, and data visualization before attempting the exam.

Renewal

Renewal rules for CompTIA Data+ are set by CompTIA. Many credentials use a multi-year cycle with continuing education, a renewal fee, or a newer exam. Confirm the current cycle on the vendor site.

Roles this credential fits

• Data concepts and environments, data acquisition and preparation, analysis, visualization and reporting, and data governance aligned with the current CompTIA Data+ V2 DA0-002 exam objectives.
• Data
• Teams standardizing on this credential for hiring or compliance

Official sources

• CompTIA certifications

Always confirm fees, dates, and eligibility on the vendor site before you register. Vendors change outlines and prices without notice.