Preparing for the Data Architect Associate (DAsP) Certification

💡Write a concise title that catches the eye

• "Designing the Framework of Data: Complete Mastery of Data Architecture"

  • Emphasizes that you can master the core concepts and design methods of data architecture.

• "First Steps as a Data Expert: New Opportunities Opening with DAsP Certification"

  • It emphasizes that obtaining certifications is the beginning of the path to becoming an expert.

• "Design the World with Data: From Data Requirements Analysis to Modeling"

  • It suggests the scope of learning centered on content applicable to practical work.

• "Everything About Data Standardization: The Secret to Building Successful Architecture"

  • We emphasize the importance of standardization so that you can feel its practical value.

• "Technology for Connecting Data: Know-how in Enterprise Architecture Design"

  • It provides an opportunity to learn about the importance of data from an enterprise-wide perspective.

💡Introduce the learning content with representative keywords.

1. Data Architecture

• Methodologies and strategies for designing data structure and management systems.

2. Enterprise Architecture

• A system that comprehensively manages and optimizes IT and data resources across the entire organization.

3. Data Requirements Analysis

• The process of collecting, analyzing, and documenting user and system requirements to connect them to data design.

4. Data Standardization

• A methodology for establishing and maintaining standards to ensure data consistency and quality.

5. Data Modeling

• Techniques of conceptual, logical, and physical modeling and database design through these approaches.

6. Reference Model

• A standardized model used in architecture design.

7. Data Governance

• A system that manages data quality, security, policies, and other aspects.

8. Metadata Management

• Systematically manage information about data to maximize data utilization.

9. Data Quality Management

• Activities and tools to ensure data accuracy, consistency, and completeness.

10. Data Integration and Connectivity

• Technology that integrates data from various sources to ensure mutual connectivity.

11. Understanding Big Data Platforms

• Large-scale data processing system architecture and design.

12. Data Security and Privacy

• Data access control, encryption, and privacy protection policies.

Practice Environment

• Operating System and Version (OS): Not applicable

• Tools used: None

• PC Specifications: Basic specification PC with internet access capability

Learning Materials

• Learning materials provided: PPT, cloud links, text, source code, sample problems, etc.

• Volume and Capacity: Learning materials provided for each section

Prerequisites and Important Notes

• No prior knowledge is required.

• Having middle and high school level mathematics knowledge and Python programming experience can be helpful for taking the course. However, since all the foundational content needed for the course is explained, there will be no problem if you take it with enthusiasm.

• The copyright of this course belongs to DFACTUP Co., Ltd., and unauthorized distribution and reproduction are prohibited. The learning materials are also copyrighted and may not be used for purposes other than personal learning.

• This entire lecture is conducted with AI voice.