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Data+ V2

CompTIA Data+ is an early-career data analytics certification that shows you have the practical skills to turn raw data into meaningful insights. Gain the confidence to analyze, interpret, and communicate data clearly, so you can solve real business problems, stand out to employers, and help your organization make smarter, data-driven decisions.

Skills you'll learn

Build a foundation in modern data concepts so you can confidently navigate analytics tools and data sources in any organization.

Transform, clean, and organize raw data to make it reliable and useful for analysis.

Apply statistical methods to extract trends, uncover insights, and support business decisions.

Use data visualizations and dashboards to translate complex results into presentations anyone can understand.

Maintain data quality, ensure compliance, and protect sensitive information in alignment with industry standards.

Solve data problems, validate reports, and resolve analytics issues as they arise on the job.

Exam Details

  • Exam version: V2

  • Exam series code: DA0-002

  • Launch date: October 14, 2025

  • Number of questions: Maximum of 90 (multiple-choice and performance-based)

  • Duration: 90 minutes

  • Passing score: 720 (on a scale of 100–900)

  • Languages: English

  • Recommended experience: 18–24 months in a data analyst or similar job role, with exposure to databases, analytical tools, basic statistics, and data visualization

  • Accreditation and benefits: ISO accredited by the ANSI National Accreditation Board (ANAB), and mapped to the NICE Framework Data Analyst (IO-WRL-001) work role.

  • Retirement: Usually three years after launch (estimated 2028)

Data+ (V2) exam objectives summary

  • Explain data concepts: Database types, data structures, file extensions, and data types.
  • Identify data sources: Databases, APIs, website data, files, logs and repositories. 
  • Recognize infrastructure concepts: Cloud, on-premise, storage, and containerization.
  • Identify data tools: Coding environments, BI software, and analysis platforms. 
  • Understand AI concepts: Identify AI models, natural language processing, and robotic automation. 

  • Use data acquisition methods: Data integration and queries to gather and combine data. 
  • Perform data exploration: Find missing values, duplication, redundancy, or outliers. 
  • Apply data transformation: Cleansing, merging, parsing, and formatting data. 

  • Communicate analysis results: Select methods for different audiences. 
  • Select statistical methods: Apply basic statistical techniques to data. 
  • Troubleshoot analysis issues: Use tools and resources to resolve problems. 

  • Create effective visuals: Use charts, maps, tables, and design elements. 
  • Deliver reports: Provide dashboards or summaries using appropriate methods. 
  • Validate reporting accuracy: Apply validation and review to solve reporting issues

  • Explain data management practices: Documentation, versioning, and data lineage. 
  • Summarize compliance requirements: Retention, audits, and regulations. 
  • Compare privacy and protection strategies: Access control, encryption, and masking. 
  • Implement quality assurance: Profiling, monitoring, and testing for data quality. 

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