Certified Data Management Professional

Pass CDMP (DAMA-DMBoK2 Revised Edition) with Confidence . And Stay Current with Industry Trends.

What you'll learn

Prepare for the CDMP Exam with Confidence

Access Over 1,000 Expertly Designed Practice Questions

Achieve a deep comprehension of DAMA-DMBoK2R to score 80% or higher in CDMP Exam

Simulate the CDMP Exam Experience

Apply Knowledge to Enhance Your Work Performance

Build a Foundation for Career Advancement

Stay Current with Industry Trends

Requirements

No prior knowledge is required! However, a basic understanding of data and data management concepts can help you progress faster.

Description

In today’s data-driven world, organizations require professionals with a deep understanding of data management principles to drive strategy, ensure compliance, and to achieve operational excellence. This course is designed to help you excel in the Certified Data Management Professional (CDMP) exam, equipping you with globally recognized credentials and mastery over key data management knowledge areas.  You will gain a comprehensive understanding of critical topics such as Data Governance, Quality, Architecture, Metadata, and Master Data Management, with practical insights into applying these concepts in real-world scenarios. Through over 1,000 expertly crafted practice questions, CDMP-like mock exams, and step-by-step guidance, you’ll confidently target a Masters-grade certification.  This course caters to a wide audience, from fresh graduates and aspiring data professionals to seasoned executives and managers. By earning the CDMP certification, you’ll stand out in the competitive job market, showcasing your expertise in managing and governing data effectively.  Whether you’re a fresh graduate who is starting your career, or enhancing your skills, or leading data-driven initiatives, this course provides the knowledge, tools, and recognition needed to succeed in the rapidly growing field of data management. Join now to next step towards becoming a sought-after data professional. Thank you and Best of Luck !

Overview

Section 1: Introduction

Lecture 1 Welcome to the Course !

Lecture 2 Who should take this couse? and The Job Market.

Lecture 3 The Book, The Exam Structure, and How to Study?

Section 2: Chapter 1 & 2: Data Management and Data Handling Ethics

Lecture 4 What is Data Management?

Lecture 5 Understanding Data Handling Ethics

Section 3: Chapter 3: Data Governance

Lecture 6 Data Governance Explained

Lecture 7 Why Data Governance is needed?

Lecture 8 Real-Life Scenario for Data Valuation and Monetization

Lecture 9 The Data Governance Organization

Lecture 10 The Data Governance Operating Framework

Lecture 11 Implementing Data Governance (Part 1)

Lecture 12 Implementing Data Governance (Part 2)

Lecture 13 Implementing Data Governance (Part 3)

Lecture 14 Implementing Data Governance (Part 4)

Section 4: Chapter 4: Data Architecture

Lecture 15 What is Data Architecture?

Lecture 16 How Data Architecture functions in an Organization?

Lecture 17 Different Architecture Domains

Lecture 18 Data Architecture Components

Lecture 19 Decoding the Enterprise Data Model

Lecture 20 The Data Flow Design

Section 5: Chapter 5: Data Modeling and Design

Lecture 21 What is Data Modeling?

Lecture 22 Data Modeling Components - Entities and Domains

Lecture 23 Data Modeling Components - Attributes (Part-1)

Lecture 24 Data Modeling Components - Attributes (Part-2)

Lecture 25 Data Modeling Components - Relationships (Part -1)

Lecture 26 Data Modeling Components - Relationships (Part -2)

Lecture 27 Data Modeling Components - Relationships (Part -3)

Lecture 28 Practical with Data - Entities and Relationships

Lecture 29 Data Normalization - 1st Normal Form (1NF)

Lecture 30 Data Normalization - 2nd Normal Form (2NF)

Lecture 31 Data Normalization - 3rd Normal Form (3NF)

Lecture 32 Conceptual Data Model (CDM)

Lecture 33 Logical Data Model (LDM)

Lecture 34 Physical Data Model (PDM)

Section 6: Chapter 6: Data Storage and Operations

Lecture 35 What is Data Storage and Operations?

Lecture 36 Common Terminologies

Lecture 37 Different Database Architectures

Lecture 38 Understanding a Blockchain Database

Lecture 39 Database Processing - ACID and BASE

Lecture 40 Databases Types and Their Organization

Section 7: Chapter 7: Data Security

Lecture 41 What is Data Security?

Lecture 42 Security Threats and Risk Assessments

Lecture 43 Cyber Threats (Part-01)

Lecture 44 Cyber Threats (Part-02)

Lecture 45 Insider Threats

Lecture 46 Common Terms of Network Security

Section 8: Chapter 8: Data Integration and Interoperability

Lecture 47 What is Data Integration and Interoperability?

Lecture 48 The Extract, Transform and Load (ETL)

Lecture 49 The ETL vs. ELT

Lecture 50 The Change Data Capture (CDC)

Lecture 51 DII Concepts (Part-01)

Lecture 52 DII Concepts (Part-02)

Section 9: Chapter 9: Document and Content Management

Lecture 53 The Document and Content Management Function

Lecture 54 The Content Management

Lecture 55 Content Metadata and Content Modeling

Lecture 56 Controlled Vocabularies

Lecture 57 Synonym Rings and Authority Lists

Lecture 58 Taxonomies and Ontologies

Section 10: Chapter 10: Reference and Master Data

Lecture 59 Understanding Reference and Master Data Management

Lecture 60 Goals and Principles

Lecture 61 Comparing Reference Data and Master Data

Lecture 62 Reference Data Structures

Lecture 63 The Master Data Management

Lecture 64 Key Processing Steps for MDM (Part - 01)

Lecture 65 Key Processing Steps for MDM (Part - 02)

Lecture 66 Key Processing Steps (Part - 03)

Lecture 67 The Entity Resolution

Lecture 68 The Entity Resolution (Continued..)

Lecture 69 The Entity Resolution (Continued..)

Lecture 70 The Entity Resolution (Continued..)

Section 11: Chapter 11: Data Warehousing and Business Intelligence

Lecture 71 Understanding Data Warehousing and Business Intelligence

Lecture 72 What is a Data Warehouse?

Lecture 73 Data Warehouse Components

Lecture 74 Understanding De-Normalization

Lecture 75 Dimensional Modeling

Lecture 76 Dimenaional Modeling Practical (Part-01)

Lecture 77 Dimenaional Modeling Practical (Part-02)

Section 12: Chapter 12: Metadata Management

Lecture 78 What is Metadata?

Lecture 79 The Metadata vs. Data

Lecture 80 Types of Metadata

Lecture 81 The Metadata Architecture (Part-01)

Lecture 82 The Metadata Architecture (Part-02)

Lecture 83 The Metadata Architecture (Part-03)

Section 13: Chapter 13: Data Quality

Lecture 84 What is Data Quality Management?

Lecture 85 Understanding Data Quality Dimensions (Part-01)

Lecture 86 Understanding Data Quality Dimensions (Part-02)

Lecture 87 Understanding Data Quality Dimensions (Part-03)

Lecture 88 Understanding Data Quality Dimensions (Part-04)

Lecture 89 Common Causes of Data Quality Problems

Lecture 90 The Shewhart / Deming Cycle

Lecture 91 Implementing Data Quality (Part-01)

Lecture 92 Implementing Data Quality (Part-02)

Section 14: Chapter 14: Big Data and Data Science

Lecture 93 What is Big Data and Data Science?

Lecture 94 The Big Data Architecture

Lecture 95 Data Science and Machine Learning

Lecture 96 Supervised Learning

Lecture 97 Unsupervised and Reinforcement Learning

Lecture 98 Data and Text Mining

Lecture 99 Data Analytics and their Types?

Section 15: The Final Exam

Lecture 100 Exam Preparations, Tips and Tricks (Part-1)

Lecture 101 Exam Preparations, Tips and Tricks (Part-2)

Lecture 102 Additional Questions & Quiz App

Lecture 103 Best of Luck

Aspiring professionals aiming for CDMP certification with Masters-grade achievement.,Students seeking to kickstart their careers in Data Management.,Individuals exploring opportunities in Data Management, Governance, and Strategy.,Experienced professionals in Data Governance, Architecture, Integration, or Master Data looking to deepen their knowledge and gain industry-recognized certification.,Executives and managers seeking strategic insights into data to enhance decision-making.,Organizations striving for data maturity through employee training and awareness programs.,Data enthusiasts passionate about mastering the foundational principles of data management.