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Certificate (Data Science)

Preparing for the Data Architect Associate (DAsP) Certification

The Data Architecture Junior Professional (DAsP) is a certification that certifies expertise in data management and design, covering data modeling, architecture design, data standardization, etc. This preparation course systematically learns from basic concepts of data architecture to practical application cases, and strengthens problem-solving skills through practical exercises. It is suitable for beginner learners who want to grow as data professionals.

(4.6) 7 reviews

49 learners

  • sdj0831
데이터모델링
기출
자격증
시험
Data Architecture Semi-Professional
Architecture
Big Data
DBMS/RDBMS
Data literacy

Reviews from Early Learners

What you will gain after the course

  • Warrior Architecture

  • Data Requirements Analysis

  • Data Standardization

  • Data modeling

Data Architecture Semi-Professional (DAsP) 🚗

In this course, the three main topics you will learn in the Data Architecture Associate program focus on understanding the fundamentals of data architecture and developing the ability to perform data management and design. Accordingly, I will introduce the following three key learning areas.

💡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.

You'll learn this kind of content

1⃣Data Architecture Concepts and Principles

  • Definition and Necessity of Data Architecture:

    • Data architecture is a blueprint that designs the structure, management, integration, and utilization of data within an organization.

    • Data strategy, understanding data utilization methods linked to business objectives.

  • Key Components of Data Architecture:

    • Data Modeling: Designing data entities, attributes, and relationships.

    • Data Governance: Quality management, security, and regulatory compliance of data.

    • Data Storage: The roles and design of storage systems such as databases, data warehouses, and data lakes.

  • Data Architecture Design Principles:

    • Modularity and Reusability.

    • Scalability and Flexibility.

    • Security and Compliance.

2⃣Data Modeling and Database Design

  • Data Modeling Basic Concepts:

    • Conceptual Data Modeling: Visual representation of high-level business requirements.

    • Logical Data Modeling: Converting business requirements into logical structures.

    • Physical Data Modeling: Concrete design for database implementation.

  • Database Design and Management:

    • Relational Database (RDBMS) Design and SQL Utilization.

    • Characteristics and examples of non-relational databases (NoSQL).

  • Normalization and Denormalization:

    • Data duplication minimization and performance optimization.

  • Data Integration:

    • Understanding Data Migration and ETL (Extract, Transform, Load) Processes.

3⃣Data Management and Governance

  • Data Quality Management:

    • The importance of data accuracy, completeness, consistency, and timeliness.

    • Data cleaning and quality inspection techniques.

  • Data Governance and Policy Development:

    • Define data ownership (Role), responsibility (Responsibility), and usage policy (Policy).

    • Data security and privacy protection (e.g., GDPR, CCPA, etc.).

  • Metadata Management:

    • The definition and role of metadata.

    • Data catalog and data lineage tracking.

  • Data Security and Privacy:

    • Data encryption, access control, and breach response measures.

💡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.

Pre-enrollment Reference Information

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.

Recommended for
these people

Who is this course right for?

  • Entry Level Data Analyst

  • IT Developers, Data Analysis Beginners

  • Data-based system related personnel

  • IT Planning and Consultant

Hello
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Reviews

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Answers

4.4

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"배움을 성장으로"

Curriculum

All

22 lectures ∙ (5hr 34min)

Course Materials:

Lecture resources
Published: 
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Reviews

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7 reviews

4.6

7 reviews

  • djccnt15님의 프로필 이미지
    djccnt15

    Reviews 20

    Average Rating 5.0

    5

    100% enrolled

    • 디펙업
      Instructor

      완강을 축하 드립니다. 앞으로도 더욱 업그레이드 된 강의로 찾아 뵐 것을 약속 드립니다. 감사합니다.

  • castle king님의 프로필 이미지
    castle king

    Reviews 1

    Average Rating 4.0

    4

    100% enrolled

    • 이준탁님의 프로필 이미지
      이준탁

      Reviews 2

      Average Rating 4.5

      4

      32% enrolled

      • hyunseok.jang님의 프로필 이미지
        hyunseok.jang

        Reviews 5

        Average Rating 3.8

        5

        32% enrolled

        상세한 설명 및 매끄러운 진행이 좋아 보입니다.

        • 디펙업
          Instructor

          학습자님 안녕하세요. 좋은 의견 감사합니다. 아무쪼록 이번 강의가 학습자님께 도움이 되기를 진심으로 바랍니다. 앞으로도 학습자님들에게 도움이 되고 쉽게 이해할 수 있는 고품질의 강의로 도움 드릴 수 있도록 최선을 다하겠습니다. 감사합니다.

      • 디펙업님의 프로필 이미지
        디펙업

        Reviews 8

        Average Rating 5.0

        5

        18% enrolled

        수강생 분들께서 필요하신 질문이나 궁금한 사항은 언제든지 의견을 남겨주시면 친절하게 답변해 드리겠습니다. 감사합니다.

        $77.00

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