Is CompTIA Data + worth it?

Yes, the Data+ is worth it for marketing analysts as well as analysts who work in sectors less commonly associated with big data. If it's your job to make data-driven business decisions but you wouldn't typically refer to yourself as a data analyst, there's still plenty on the Data+ for you and your career.

What are the prerequisites to learn CompTIA Data?

CompTIA Data+ recommends 18 to 24 months of experience in a report/business analyst job role, exposure to databases and analytics tools, a basic understanding of statistics, and data visualization experience.

Is CompTIA Certification high in demand?

CASP, or CompTIA Advanced Security Practitioner, is one of the most sought-after certifications and many IT professionals aspire to have it in their toolbox.

Does CompTIA pays well?

How much does CompTIA in the United States pay? The average CompTIA salary ranges from approximately $49,000 per year for Admissions Advisor to $179,155 per year for Creative Director.

Are CompTIA exams easy?

Actually, CompTIA A+ is not difficult because many can pass it, and it is known as IT easiest certification. It may be difficult for first-time certification exam takers but more accessible for those who already have a basic knowledge of IT. It covers topics at a basic level.

CompTIA Data+ DA0-001 Training Course- Henry Harvin®

Module 1: Data Concepts & Environments
In this module, learn data concepts and environments including data schemas, dimensions, different data types, and more
- Identify basic concepts of data schemas and dimensions
- Compare and contrast different data types
- Compare and contrast common data structures and file formats
Module 2: Data Mining
In this module learn about data mining including data acquisition concepts, profiling datasets, data manipulation techniques, and more
- Explain data acquisition concepts
- Identify common reasons for cleansing and profiling datasets
- Given a scenario, execute data manipulation techniques
- Explain common techniques for data manipulation and query optimization
Module 3: Data Analysis
In this module, learn about data analysis techniques, statistical methods, types of analysis, and more
- Given a scenario, apply the appropriate descriptive statistical methods
- Explain the purpose of inferential statistical methods
- Summarize types of analysis and key analysis techniques
- Identify common data analytics tools
Module 4: Visualization
In this module, learn about translate business requirements to form a report, dashboard development, type of visualization, and more
- Given a scenario, translate business requirements to form a report
- Given a scenario, use appropriate design components for reports and dashboards
- Given a scenario, use appropriate methods for dashboard development
- Given a scenario, apply the appropriate type of visualization
- Compare and contrast types of reports
Module 5: Data Governance, Quality and Controls
In this module, learn about the concepts of data governance, data quality control, data management, and know how to apply them in given situations
- Summarize important data governance concepts
- Given a scenario, apply data quality control concepts
- Explain master data management (MDM) concepts