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CompTIA Data+ is the industry's standard for preparing for entry-level IT jobs. CompTIA Data+ validates that you have the skills needed to succeed in a technical career, and the certification can help you advance your career. CompTIA DA0-001 exam dumps will help your to make you career.
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The CompTIA Data+ certification covers topics such as storage networks, desktop support services, operating systems and more. It also includes a section on cloud computing, which helps prepare candidates for future job roles that may require them to use cloud-based technologies.
Reference: https://www.comptia.org/training/books/data-da0-001-study-guide
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CompTIA DA0-001 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Data Concepts and Environments - 15% | |
| Identify basic concepts of data schemas and dimensions. | - Databases
- Data mart/data warehousing/data lake
- Schema concepts
- Slowly changing dimensions
|
| Compare and contrast different data types. | - Date - Numeric - Alphanumeric - Currency - Text - Discrete vs. continuous - Categorical/dimension - Images - Audio - Video |
| Compare and contrast common data structures and file formats. | - Structures
- Data file formats
|
Data Mining - 25% | |
| Explain data acquisition concepts. | - Integration
- Data collection methods
|
| Identify common reasons for cleansing and profiling datasets. | - Duplicate data - Redundant data - Missing values - Invalid data - Non-parametric data - Data outliers - Specification mismatch - Data type validation |
| Given a scenario, execute data manipulation techniques. | - Recoding data
- Derived variables |
| Explain common techniques for data manipulation and query optimization. | - Data manipulation
- Query optimization
|
Data Analysis - 23% | |
| Given a scenario, apply the appropriate descriptive statistical methods. | - Measures of central tendency Mean Median Mode - Measures of dispersion
- Frequencies/percentages |
| Explain the purpose of inferential statistical methods. | - t-tests - Z-score - p-values - Chi-squared - Hypothesis testing
- Simple linear regression |
| Summarize types of analysis and key analysis techniques. | - Process to determine type of analysis
- Type of analysis
|
| Identify common data analytics tools. | - Structured Query Language (SQL) - Python - Microsoft Excel - R - Rapid mining - IBM Cognos - IBM SPSS Modeler - IBM SPSS - SAS - Tableau - Power BI - Qlik - MicroStrategy - BusinessObjects - Apex - Dataroma - Domo - AWS QuickSight - Stata - Minitab |
Visualization - 23% | |
| Given a scenario, translate business requirements to form a report. | - Data content - Filtering - Views - Date range - Frequency - Audience for report
|
| Given a scenario, use appropriate design components for reports and dashboards. | - Report cover page
- Design elements
- Documentation elements
|
| Given a scenario, use appropriate methods for dashboard development. | - Dashboard considerations
- Development process
Delivery considerations
|
| Given a scenario, apply the appropriate type of visualization. | - Line chart - Pie chart - Bubble chart - Scatter plot - Bar chart - Histogram - Waterfall - Heat map - Geographic map - Tree map - Stacked chart - Infographic - Word cloud |
| Compare and contrast types of reports. | - Static vs. dynamic reports
- Ad-hoc/one-time report
- Tactical/research report |
Data Governance, Quality, and Controls - 14% | |
| Summarize important data governance concepts. | - Access requirements
- Security requirements
- Storage environment requirements
- Use requirements
- Entity relationship requirements
- Data classification
- Jurisdiction requirements
- Data breach reporting
|
| Given a scenario, apply data quality control concepts. | - Circumstances to check for quality
- Automated validation
- Data quality dimensions
- Data quality rule and metrics
- Methods to validate quality
|
| Explain master data management (MDM) concepts. | - Processes
- Circumstances for MDM
|
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