Certified Data Management Professional (Cdmp) - Associate





What you'll learn
Master the core principles of data governance and the roles and responsibilities involved.
Understand the DAMA-DMBOK framework and its importance in data management.
Learn the structure and certification levels of the CDMP exam.
Explore strategies for preparing and studying for the CDMP certification exam.
Grasp the foundational concepts of data architecture, including logical and physical data models.
Design scalable data systems that meet organizational needs.
Gain a deep understanding of data modeling, including normalization and denormalization techniques.
Develop expertise in data storage models, data retention, backup, and recovery strategies.
Understand the principles of data security and how to mitigate risks in data management.
Implement best practices for ensuring data quality and improving data integrity.
Explore the concepts of master and reference data management and their role in data consistency.
Learn the fundamentals of metadata management and how it enhances data accessibility and governance.
Understand the role of data warehousing and business intelligence in strategic decision-making.
Explore emerging technologies like AI and big data and their impact on data management.
Dive into cloud data management and its benefits for scalable and secure data storage.
Understand ethical data management practices and how to ensure regulatory compliance.
Requirements
No Prerequisites.
Description
This course offers an in-depth exploration of the core principles and frameworks surrounding data management, with a specific emphasis on preparing students for the CDMP (Certified Data Management Professional) certification. The course is designed to provide a comprehensive overview of the various aspects of data management, including governance, architecture, modeling, security, quality, and more. While the course encompasses the theory of these data management concepts, it also provides valuable insights into how they can be applied in real-world scenarios, making it an essential resource for those looking to deepen their understanding of data management or prepare for the CDMP exam.Beginning with an introduction to the CDMP certification process, students will gain a detailed understanding of the certification levels, exam structure, and essential study strategies. This foundational knowledge not only prepares students for the certification itself but also provides a solid framework for comprehending the broader field of data management. In particular, students will appreciate the subtle focus on theoretical aspects that underpin data management, allowing them to explore the key concepts without the distraction of immediate hands-on applications.The course delves into data governance, one of the most crucial pillars of effective data management. Students will examine the roles and responsibilities that come with governance, as well as the policies, procedures, and frameworks that support a strong data governance strategy. Understanding governance frameworks is essential for ensuring that data remains secure, accurate, and compliant with industry standards. Students will learn how governance ties into the overall architecture of data systems and how it forms the backbone of a sustainable data management strategy.Next, the course takes a closer look at data architecture, providing insights into how data is structured, modeled, and managed across an organization. Key concepts such as logical versus physical data models and the principles of designing scalable data systems are explored in detail. Students will also study enterprise architecture and its integration with data management practices, which is crucial for organizations aiming to align their data systems with strategic business goals. This section encourages students to think critically about the theoretical models that shape modern data architecture and how these models can be adapted to meet an organization's unique needs.Data modeling and design are fundamental to ensuring that data is both useful and efficient in meeting organizational objectives. The course covers essential topics such as normalization, denormalization, and data relationships, providing students with the knowledge needed to design and optimize data models for various industries. In doing so, students will gain an understanding of best practices in data design, with an emphasis on conceptual, logical, and physical data models, further cementing their grasp of data management theory.Students will also explore the intricacies of data storage and operations, including storage models, techniques, and policies for data retention, backup, and recovery. The importance of data security management is also highlighted, focusing on principles, policies, and strategies for mitigating risks and ensuring regulatory compliance. In today's digital age, where data breaches and cybersecurity threats are constant concerns, understanding these security principles is vital for anyone working in data management.Furthermore, the course covers essential topics such as data quality management, metadata management, and reference and master data management. Each of these areas contributes to the overall goal of maintaining high standards of data integrity, accessibility, and usability. By the end of these sections, students will be equipped with the knowledge to assess and improve data quality, manage metadata repositories, and ensure that master and reference data are handled efficiently.As the course progresses, students will learn about data warehousing and business intelligence, which are critical for leveraging data in decision-making processes. The course also addresses emerging trends in data management, including the role of big data, artificial intelligence, and cloud technologies, which are reshaping the future of data systems.In summary, this course offers a thorough examination of data management principles with a focus on preparing students for CDMP certification. Through its structured approach to theoretical concepts, students will build a robust foundation in data management, which can be applied to a wide range of professional settings. Whether you are new to the field or looking to formalize your expertise, this course provides the essential knowledge and tools needed to excel in the dynamic and evolving world of data management.
Overview
Section 1: Course Resources and Downloads
Lecture 1 Course Resources and Downloads
Section 2: Introduction to CDMP and Data Management
Lecture 2 Section Introduction
Lecture 3 Overview of CDMP Certification
Lecture 4 Case Study: Empowering Data Management Careers
Lecture 5 Introduction to Data Management
Lecture 6 Case Study: Enhancing RetailHub's Data Management
Lecture 7 The DAMA-DMBOK Framework
Lecture 8 Case Study: Transforming Data Management at TechNova
Lecture 9 Certification Levels and Exam Structure
Lecture 10 Case Study: Achieving the CDMP
Lecture 11 Study Strategies for the CDMP Exam
Lecture 12 Case Study: Strategic Mastery
Lecture 13 Section Summary
Section 3: Data Governance
Lecture 14 Section Introduction
Lecture 15 Introduction to Data Governance
Lecture 16 Case Study: Empowering RetailNet
Lecture 17 Governance Roles and Responsibilities
Lecture 18 Case Study: Enhancing Data Governance
Lecture 19 Data Stewardship and Accountability
Lecture 20 Case Study: Implementing Data Governance at NexFinance
Lecture 21 Policies, Procedures, and Standards
Lecture 22 Case Study: TechCo's Data Governance Overhaul
Lecture 23 Governance Frameworks and Best Practices
Lecture 24 Case Study: Implementing Data Governance Frameworks
Lecture 25 Section Summary
Section 4: Data Architecture
Lecture 26 Section Introduction
Lecture 27 Introduction to Data Architecture
Lecture 28 Case Study: Data Architecture Excellence
Lecture 29 Data Architecture Principles
Lecture 30 Case Study: Transforming Data Architecture
Lecture 31 Logical vs. Physical Data Models
Lecture 32 Case Study: Optimizing CRM Data Management
Lecture 33 Enterprise Architecture and Data Management
Lecture 34 Case Study: Strategic Integration of Enterprise Architecture and Data Management
Lecture 35 Designing Scalable Data Systems
Lecture 36 Case Study: Balancing Vertical and Horizontal Scalability
Lecture 37 Section Summary
Section 5: Data Modeling and Design
Lecture 38 Section Introduction
Lecture 39 Introduction to Data Modeling
Lecture 40 Case Study: Optimizing Data Modeling for Business Growth
Lecture 41 Conceptual, Logical, and Physical Data Models
Lecture 42 Case Study: Optimizing Patient Management Systems
Lecture 43 Normalization and Denormalization
Lecture 44 Case Study: Balancing Normalization and Denormalization
Lecture 45 Data Relationships and Entities
Lecture 46 Case Study: Building a Scalable Data Model
Lecture 47 Best Practices in Data Design
Lecture 48 Case Study: Transforming Data Management
Lecture 49 Section Summary
Section 6: Data Storage and Operations
Lecture 50 Section Introduction
Lecture 51 Introduction to Data Storage
Lecture 52 Case Study: Optimizing Data Storage
Lecture 53 Storage Models and Techniques
Lecture 54 Case Study: Strategizing Scalable and Secure Data Storage
Lecture 55 Data Retention Policies
Lecture 56 Case Study: Developing a Robust Data Retention Policy
Lecture 57 Data Backup and Recovery
Lecture 58 Case Study: Strengthening Data Resilience
Lecture 59 Data Archiving and Deletion
Lecture 60 Case Study: Optimizing Data Management
Lecture 61 Section Summary
Section 7: Data Security Management
Lecture 62 Section Introduction
Lecture 63 Introduction to Data Security
Lecture 64 Case Study: Enhancing Data Security
Lecture 65 Data Security Principles and Policies
Lecture 66 Case Study: Enhancing Data Security
Lecture 67 Data Access Control and Encryption
Lecture 68 Case Study: Integrated Data Access Control and Encryption Strategies
Lecture 69 Security Risks and Mitigation Strategies
Lecture 70 Case Study: Enhancing Data Security
Lecture 71 Regulatory Compliance and Data Privacy
Lecture 72 Case Study: TechNova Data Breach
Lecture 73 Section Summary
Section 8: Data Quality Management
Lecture 74 Section Introduction
Lecture 75 Introduction to Data Quality
Lecture 76 Case Study: Enhancing Data Quality in Healthcare
Lecture 77 Data Quality Dimensions
Lecture 78 Case Study: Optimizing Data Quality Dimensions
Lecture 79 Data Quality Assessment Techniques
Lecture 80 Case Study: Enhancing Data Quality
Lecture 81 Tools and Processes for Data Quality Improvement
Lecture 82 Case Study: Enhancing Data Quality at TechNova
Lecture 83 Implementing a Data Quality Program
Lecture 84 Case Study: Enhancing Decision-Making
Lecture 85 Section Summary
Section 9: Reference and Master Data Management
Lecture 86 Section Introduction
Lecture 87 Introduction to Master and Reference Data
Lecture 88 Case Study: Transforming Data Management
Lecture 89 Differences Between Master and Reference Data
Lecture 90 Case Study: Enhancing GlobalTech's Data Management
Lecture 91 Master Data Management Frameworks
Lecture 92 Case Study: Optimizing Healthcare and Retail Operations
Lecture 93 Reference Data Standardization
Lecture 94 Case Study: Enhancing Data Integrity and Compliance
Lecture 95 Tools for Managing Master and Reference Data
Lecture 96 Case Study: Implementing Master and Reference Data Management
Lecture 97 Section Summary
Section 10: Metadata Management
Lecture 98 Section Introduction
Lecture 99 Introduction to Metadata Management
Lecture 100 Case Study: Enhancing Retail Success through Robust Metadata Management
Lecture 101 Types of Metadаta: Descriptive, Structural, and Administrative
Lecture 102 Case Study: Transformative Metadata Management
Lecture 103 Metadata Repositories and Standards
Lecture 104 Case Study: Enhancing Data Management
Lecture 105 Metadata Governance
Lecture 106 Case Study: TechNova's Metadata Governance
Lecture 107 The Role of Metadata in Data Integration
Lecture 108 Case Study: Leveraging Metadata for Efficient and Quality-Driven Data
Lecture 109 Section Summary
Section 11: Data Warehousing and Business Intelligence
Lecture 110 Section Introduction
Lecture 111 Introduction to Data Warehousing
Lecture 112 Case Study: Strategic Data Warehousing
Lecture 113 Data Marts and Data Lakes
Lecture 114 Case Study: Balancing Data Marts and Data Lakes for Scalable Analytics
Lecture 115 Business Intelligence Principles
Lecture 116 Case Study: Unlocking the Full Potential of Business Intelligence at TechNova
Lecture 117 Data Warehousing Models: Star and Snowflake
Lecture 118 Case Study: Optimizing Data Warehousing
Lecture 119 Reporting and Analytics in BI
Lecture 120 Case Study: Transforming Patient Care Through Business Intelligence
Lecture 121 Section Summary
Section 12: Data Integration and Interoperability
Lecture 122 Section Introduction
Lecture 123 Introduction to Data Integration
Lecture 124 Case Study: TechFusion's Comprehensive Data Integration Strategy
Lecture 125 Data Sources and Extraction Techniques
Lecture 126 Case Study: Integrating Diverse Data Sources for Enhanced Business Insights
Lecture 127 Data Transformation and Loading (ETL)
Lecture 128 Case Study: Optimizing ETL for Data Integration
Lecture 129 Data Interoperability Standards
Lecture 130 Case Study: Enhancing Patient Care Through HL7 Interoperability
Lecture 131 Managing Data Silos
Lecture 132 Case Study: Overcoming Data Silos
Lecture 133 Section Summary
Section 13: Document and Content Management
Lecture 134 Section Introduction
Lecture 135 Introduction to Document Management
Lecture 136 Case Study: Transforming Healthcare Efficiency
Lecture 137 Content Management Systems (CMS)
Lecture 138 Case Study: Transforming E-Commerce Efficiency
Lecture 139 Managing Unstructured Data
Lecture 140 Case Study: Optimizing Unstructured Data Management
Lecture 141 Version Control and Document Security
Lecture 142 Case Study: Integrating Version Control and Document Security
Lecture 143 Document Classification and Retrieval
Lecture 144 Case Study: Optimizing Document Management with Machine Learning
Lecture 145 Section Summary
Section 14: Big Data and Emerging Technologies
Lecture 146 Section Introduction
Lecture 147 Introduction to Big Data
Lecture 148 Case Study: Strategic Evolution at TechNova
Lecture 149 The Role of Big Data in Data Management
Lecture 150 Case Study: TechNova's Big Data Transformation
Lecture 151 Big Data Storage and Processing
Lecture 152 Case Study: Strategic Innovations in Big Data Management
Lecture 153 Emerging Data Technologies (AI, Blockchain)
Lecture 154 Case Study: Integrating AI and Blockchain
Lecture 155 Implications of Big Data for Data Management
Lecture 156 Case Study: Transforming Data Management
Lecture 157 Section Summary
Section 15: Data Management Ethics and Compliance
Lecture 158 Section Introduction
Lecture 159 Introduction to Data Ethics
Lecture 160 Case Study: Ethical Data Management in Tech
Lecture 161 Ethical Data Use and Decision Making
Lecture 162 Case Study: Navigating Ethical Challenges
Lecture 163 Regulatory Frameworks (GDPR, HIPAA)
Lecture 164 Case Study: Navigating GDPR and HIPAA
Lecture 165 Legal Considerations in Data Management
Lecture 166 Case Study: Ensuring GDPR and HIPAA Compliance in Global Healthcare
Lecture 167 Building an Ethical Data Culture
Lecture 168 Case Study: Building an Ethical Data Culture
Lecture 169 Section Summary
Section 16: Emerging Trends in Data Management
Lecture 170 Section Introduction
Lecture 171 Introduction to Data Management Trends
Lecture 172 Case Study: Mastering Data Management
Lecture 173 Data Governance in the Age of Big Data
Lecture 174 Case Study: TechNova's Strategic Overhaul
Lecture 175 Artificial Intelligence and Data Management
Lecture 176 Case Study: TechNova's AI-Driven Transformation
Lecture 177 Cloud Data Management
Lecture 178 Case Study: Transforming E-Commerce
Lecture 179 Data Privacy and Ethical Challenges in the Digital Age
Lecture 180 Case Study: ZypherTech Data Breach
Lecture 181 Section Summary
Section 17: Course Summary
Lecture 182 Conclusion
Aspiring data management professionals seeking to earn the CDMP Associate certification.,IT and data professionals looking to enhance their knowledge of data governance, architecture, and security.,Individuals transitioning into data management roles who want a comprehensive understanding of key principles.,Business analysts and data analysts aiming to improve their data modeling and quality management skills.,Project managers and team leads overseeing data-driven projects and seeking to improve data strategies.,Recent graduates in IT, computer science, or business-related fields who want to specialize in data management.,Professionals interested in staying updated on emerging trends like AI, big data, and cloud technologies in data management.





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