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Objectives of CompTIA DA0-001 Exam
In this section, we will list the objectives you should know in order to pass your CompTIA Data+ certification exam.
The first objective is to understand how to become a successful IT professional with data skills.
The second objective is to learn about different types of data, including structured and unstructured data.
The third objective is to be able to demonstrate your knowledge of at least two types of data storage: online and offline.
The fourth objective is to be able to describe how a client can access information from various sources through technology such as social media or the web. CompTIA DA0-001 exam dumps he best way to prepare for the exams.
The fifth objective is for you to be able to apply concepts related to using software applications such as Microsoft Office suite, Google Docs, and Adobe Creative Cloud.
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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