What does the CompTIA Data+ exam cover?
The CompTIA Data+ DA0-001 exam covers data concepts and environments, mining and data management, data analysis, visualization, and data governance. It is an entry-to-intermediate level certification for data analysts and business intelligence professionals. The exam costs $369 USD with a passing score of 675 out of 900 and requires 18-24 months of data analysis experience.
The CompTIA Data+ DA0-001 certification validates foundational to intermediate data analysis skills, covering the full data analyst workflow from data collection and preparation through analysis, visualization, and governance. It is vendor-neutral, covering concepts applicable across Excel, SQL, Python, Tableau, Power BI, and other data tools.
Data analysis is one of the fastest-growing IT specializations, with the U.S. Bureau of Labor Statistics projecting 23% growth in data analyst roles through 2032. Data+ provides a structured credential demonstrating data literacy and analytical capability. The exam costs $369 USD and requires a passing score of 675 out of 900.
Exam Overview
| Detail | Information |
|---|---|
| Exam Code | DA0-001 |
| Full Name | CompTIA Data+ |
| Number of Questions | Maximum 90 |
| Time Limit | 90 minutes |
| Passing Score | 675/900 |
| Cost | $369 USD |
| Prerequisites | 18-24 months data analysis experience recommended |
| Validity | 3 years |
The exam covers five domains:
- Data concepts and environments (15%)
- Data mining (25%)
- Data analysis (23%)
- Visualization (23%)
- Data governance, quality, and controls (14%)
"Data+ fills the gap between knowing how to use a specific tool like Excel or Tableau and understanding the broader context of data analysis work -- data quality, governance, statistical concepts, and business communication. It validates that a data analyst can do the full job, not just run queries." -- CompTIA data certification community
Domain 1: Data Concepts and Environments (15%)
Data Types
| Data Type | Description | Examples |
|---|---|---|
| Structured | Organized in rows and columns with defined schema | Database tables, CSV files |
| Unstructured | No predefined structure | Images, videos, emails, social media posts |
| Semi-structured | Some structure but not fully relational | JSON, XML, email with headers |
| Quantitative | Numeric, measurable | Revenue, temperature, count |
| Qualitative | Descriptive, categorical | Customer satisfaction level, product category |
| Time-series | Sequential data points over time | Stock prices, sensor readings |
| Geospatial | Location-based data | GPS coordinates, zip codes |
Database Fundamentals
Relational databases store data in tables with primary and foreign key relationships. SQL is the standard query language.
Non-relational databases (NoSQL):
- Document stores (MongoDB): JSON-like documents
- Key-value stores (Redis): Simple key-value pairs
- Column-family stores (Cassandra): Wide columns for analytics
- Graph databases (Neo4j): Nodes and edges for connected data
OLTP vs. OLAP:
- OLTP (Online Transaction Processing): Optimized for frequent read/write operations (row-based storage)
- OLAP (Online Analytical Processing): Optimized for complex queries and aggregations (columnar storage)
Domain 2: Data Mining (25%)
Data Collection Methods
- Surveys and questionnaires: Structured collection from human respondents
- APIs: Programmatic data collection from web services
- Web scraping: Automated extraction from web pages
- Database queries: Direct extraction from operational systems
- IoT sensors: Continuous data from physical devices
- Log files: Automatically generated records from applications
Data Preparation and Transformation
ETL (Extract, Transform, Load): The core process for moving data from source systems to analytics environments:
- Extract: Pulling data from source systems (databases, APIs, files)
- Transform: Cleaning, normalizing, and reshaping data for analysis
- Load: Writing transformed data to the target system
Common data quality issues:
| Issue | Description | Resolution |
|---|---|---|
| Missing values | Null or blank fields | Imputation, removal, or flagging |
| Duplicate records | Same entity appearing multiple times | Deduplication using key fields |
| Inconsistent formatting | "Jan 1, 2025" vs "2025-01-01" | Standardize to consistent format |
| Outliers | Values far outside normal range | Investigation; retain if valid, remove if error |
| Incorrect data types | Numbers stored as text | Type conversion |
SQL for Data Mining
-- Basic aggregation
SELECT region, SUM(revenue) AS total_revenue, AVG(revenue) AS avg_revenue
FROM sales
WHERE sale_date BETWEEN '2024-01-01' AND '2024-12-31'
GROUP BY region
HAVING SUM(revenue) > 100000
ORDER BY total_revenue DESC;
-- Window functions
SELECT
employee_id,
sale_amount,
ROW_NUMBER() OVER (PARTITION BY region ORDER BY sale_amount DESC) AS rank_in_region,
SUM(sale_amount) OVER (PARTITION BY region) AS region_total
FROM sales;
-- Subquery
SELECT customer_id, total_orders
FROM (
SELECT customer_id, COUNT(*) AS total_orders
FROM orders
GROUP BY customer_id
) subq
WHERE total_orders > 10;
Domain 3: Data Analysis (23%)
Statistical Concepts
Measures of central tendency:
- Mean: Arithmetic average; affected by outliers
- Median: Middle value; robust to outliers
- Mode: Most frequent value
Measures of spread:
- Standard deviation: Average distance from the mean; low = data clustered, high = data spread
- Variance: Standard deviation squared
- Range: Maximum - minimum
- Interquartile range (IQR): Q3 - Q1; contains the middle 50% of values
Analysis Techniques
Trend analysis: Identifying patterns over time in time-series data. Simple moving averages smooth out noise to reveal underlying trends.
Segmentation analysis: Dividing data into meaningful groups for comparison. Customer segmentation by demographics, behavior, or value.
Regression analysis: Modeling the relationship between variables:
- Linear regression: Predicts a continuous outcome from one or more predictors
- Logistic regression: Predicts a binary outcome (yes/no, churned/retained)
- Correlation coefficient (r): Measures strength of linear relationship (-1 to +1)
Cohort analysis: Tracking a group with a common characteristic over time (e.g., all customers who joined in Q1 2024 -- how do their retention rates evolve monthly?).
Domain 4: Visualization (23%)
Choosing the Right Chart Type
| Data Relationship | Best Chart Type |
|---|---|
| Composition (part-to-whole) | Pie chart, donut chart, stacked bar |
| Comparison (between categories) | Bar chart, column chart, lollipop chart |
| Distribution (spread of values) | Histogram, box plot, violin plot |
| Relationship (correlation) | Scatter plot, bubble chart |
| Trend (change over time) | Line chart, area chart |
| Geographic distribution | Choropleth map, bubble map |
Dashboard Design Principles
Effective dashboard design:
- Hierarchy: Most important metrics prominently displayed
- Simplicity: Remove chartjunk (unnecessary gridlines, 3D effects, excess colors)
- Consistency: Same colors represent the same categories throughout
- Context: Include benchmarks, targets, and prior-period comparisons
- Interactivity: Filters and drill-downs for exploration
Common visualization tools: Tableau, Microsoft Power BI, Google Looker Studio, Excel, Python (matplotlib, seaborn, plotly).
"The visualization domain tests whether candidates understand not just how to create charts but which chart type communicates the intended insight most clearly. Showing monthly revenue trend in a pie chart, or showing part-to-whole composition in a line chart, are common mistakes that the exam specifically tests candidates' ability to identify and correct." -- Data analysis training community
Domain 5: Data Governance, Quality, and Controls (14%)
Data Governance Framework
Data governance establishes policies and standards for data management:
- Data ownership: Assigning accountability for specific data domains
- Data stewardship: Day-to-day management and quality maintenance
- Data catalog: Inventory of data assets with metadata, lineage, and quality metrics
- Data classification: Categorizing data by sensitivity (public, internal, confidential, restricted)
Privacy Regulations
| Regulation | Jurisdiction | Scope |
|---|---|---|
| GDPR | European Union | Personal data of EU residents |
| CCPA | California, USA | Personal data of California residents |
| HIPAA | United States | Protected health information |
| PIPEDA | Canada | Personal information in commercial activity |
Data Quality Dimensions
Six dimensions of data quality:
- Accuracy: Data correctly represents the real world
- Completeness: All required data is present
- Consistency: Data is the same across systems
- Timeliness: Data is available when needed
- Uniqueness: No duplicate records
- Validity: Data conforms to required formats and rules
Frequently Asked Questions
What tools should I know for the Data+ exam? The exam is tool-agnostic but tests concepts that are implemented in common tools. SQL knowledge is essential. Familiarity with a business intelligence tool like Power BI or Tableau helps with visualization questions. Basic statistical concepts from any statistics course apply directly. Python and Excel are helpful but not required to pass the exam.
How does Data+ compare to the Google Data Analytics certificate? The Google Data Analytics certificate is a comprehensive career training program (approximately 6 months) covering data analysis tools including SQL, R, and Tableau. CompTIA Data+ is a proctored certification exam validating existing skills without providing training. Data+ is more rigorous as a standalone credential recognized in IT hiring contexts. Many candidates complete Google Data Analytics training and then validate those skills with Data+.
Is Data+ better than earning a college degree in data analytics? These are complementary, not competing credentials. A data analytics degree provides mathematical and statistical depth, programming skills, and domain knowledge over 2-4 years. Data+ is a focused credential that can be earned in weeks and validates specific skills recognized by hiring managers in IT organizations. Candidates with degrees benefit from also holding Data+ as evidence their skills meet industry standards, while Data+ alone provides an accessible entry point for career changers.
References
- CompTIA. (2025). CompTIA Data+ DA0-001 Exam Objectives. https://www.comptia.org/certifications/data
- CompTIA. (2023). CompTIA Data+ Study Guide. Sybex.
- Chartio. (2024). Data Analysis Best Practices. https://chartio.com/learn/data-analytics/
- DAMA International. (2017). DAMA-DMBOK: Data Management Body of Knowledge (2nd ed.). Technics Publications.
- Few, S. (2012). Show Me the Numbers: Designing Tables and Graphs to Enlighten. Analytics Press.
- Bureau of Labor Statistics. (2024). Occupational Outlook: Data Scientists. https://www.bls.gov/ooh/math/data-scientists.htm