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DMS0: DMBoK and CDMP Preparation: Data Modeling Specialist Learning Plan

In the DMBoK and CDMP Preparation: Data Modeling Specialist Learning Plan, instructor Christopher Bradley (a CDMP Fellow) addresses the core Data Management discipline of Data Modeling. Often misunderstood and relegated to just the technical aspect of “database design,” Data Modeling is one of the most important disciplines of Data Management. This five-course Learning Plan introduces students to Data Modeling, its purpose, the different types of models, how to construct and read a data model, and the wider use of data models beyond the traditional area of database design. It also helps prepare attendees to take the Certified Data Management Professional (CDMP) Data Modeling Specialist exam, which is necessary to attain the Practitioner and Master levels.

The DMBoK and CDMP Preparation: Data Modeling Specialist Learning Plan explains the fundamental building blocks of Data Modeling. It will help students to understand the differences between relational and dimensional models, and be able to describe the purpose of enterprise, conceptual, logical, and physical data models. Attendees will learn how to create conceptual and logical models, and understand the compromises frequently necessary for good physical data models. They will also learn the different approaches for fact finding and how to apply normalization techniques.

Often misunderstood and relegated to just the technical aspect of “database design,” Data Modeling is one of the most important disciplines of Data Management. Taught by instructor Christopher Bradley (a CDMP Fellow), this course introduces students to Data Modeling, its purpose, the different types of models, how to construct and read a data model, and the wider use of data models beyond the traditional area of database design. It also goes into best practices, practical applications, and implementation of Data Modeling at enterprise levels.

The training presents methods and practices for addressing key Data Modeling challenges. It is grounded in the industry standard DAMA International Data Management Body of Knowledge (DMBoK).

The training also prepares attendees to take the Certified Data Management Professional (CDMP) Data Modeling Specialist exam that is necessary for the attainment of the Practitioner and Master levels.

The DAMA International-endorsed live online training was developed with DAMA International, DATAVERSITY, and Christopher Bradley, the former VP of Professional Development at DAMA.

CDMP Specialist Exam Preparation Information:

Understand the in-depth information required for the CDMP Data Modeling Specialist exam
Throughout the course, practice by taking sample questions in each section
There is a full, comprehensive practice test at the end of Course 5, each of the other courses have shorter review tests

Learning Plan Outcomes:

Get prepared for taking the Data Modeling CDMP Specialist exam
Learn about the need for and the application of data models
See the areas where Data Modeling adds value to Data Management activities
Understand the critical role of data models in Master Data Management and Data Governance
Understand the difference between enterprise, conceptual, logical, physical, and dimensional data models
Learn the best practices for developing data models that can be read by humans
Through practical examples, learn how to apply different techniques in Data Modeling

Who Should Take This Learning Plan?

This Learning Plan is intended for business and IT professionals at all levels who have been charged with investigating or implementing Data Modeling in their organizations and who seek to gain an overview of the different disciplines of Data and Information Management. It was also developed as a preparation course for people wishing to take the CDMP Data Modeling Specialist exam, and much of the materials focuses on the necessary skills to pass that exam. It does assume general business knowledge, but not specific technical knowledge or experience. It is appropriate for executives, departmental and/or project managers, data and enterprise architects, consultants, data modelers, BI and data warehouse developers, data and business analysts, DBAs, technical staff, and anyone else interested and involved in data. The course is not designed for any specific business domain, and as such is applicable to any business function in need of more reliable information, such as finance, manufacturing, human resources, analytics, operations, and more.

Learning Plan Price: $799
Individual Course Price: $199
Learning Plan CEUs: 10.0 hours

Each Course Includes:

A 63- to 138-minute educational training video
A 14- to 34-question exam
After Course 5 (the final course in the Learning Plan), there is a 34-question CDMP-style practice exam
The other final exams and quizzes for each course are content-based practice tests
“Check for Understanding” quizzes after each course section
Presentation slides to assist in studying for the CDMP
Self-paced and on-demand e-learning
Unlimited course access

Courses within the DMBoK and CDMP – Data Modeling Specialist Learning Plan:

CDMP Overview, Data, Data Modeling Drivers, and Goals
Metadata, Subtypes and Supertypes, Entities, Attributes, Domains, and Relationships
Keys, Approaches, and Normalization
Abstraction, Denormalization, Lineage, Data Models, Views, and Partitioning
Dimensional Models, Best Practices, and Notations

We offer several bulk licensing options for corporate and group use.

DMBoK and CDMP Preparation: Data Modeling Specialist Learning Plan

Course 1: CDMP Overview, Data, Data Modeling Drivers, and Goals

DMBoK and CDMP: Data Modeling Specialist Learning Plan – Courses
Course Goals
CDMP Overview and Exam Coverage
- CDMP Associate
- CDMP Practitioner
- CDMP Master
- Question Bank
- Data Modeling Exam Breakdown
What is Data Management Critical?
- The Problem
- Clarity and Definition
Big Data and Data as an Asset
- The Problem
- Why Produce a Data Model?
- The Information Value Chain
- Data, Information and Knowledge (Information Value Chain)
- Business Environmental Elements
- Assets
  - Data as an Asset
- Data Model Levels
  - Models in the Real World
- Entity Relationship Symbols (Information Engineering)
- What is a Data Model?
  - What a Data Model Represents
- Data Management Disciplines (KA’s)
- Data Modeling and Design
What a Data Model Can Do for You
- Why Produce a Data Model?
- The Importance of Modeling
- Where Does Data Modeling Fit?
- Data Modeling Enabling Core Business Considerations
Fundamentals of Data Modeling – Concepts and Definitions
- What a Data Model Represents
- Types of Data Modelled
- What Makes a Good Definition
- What is a Conceptual Data Model?
Quiz – Course 1
- Answer Key

Course 2: Metadata, Subtypes & Supertypes, Entities, Attributes, Domains, and Relationships

DMBoK And CDMP Preparation: Data Modeling Specialist Learning Plan – Courses
Course Goals
Exam Breakdown, Data, and Metadata
- Data Modeling Exam Breakdown
- What is Data?
- Data Vs. Metadata
- What is Metadata?
  - Metadata
  - Encountering Metadata Everyday
  - Metadata Adds Context and Definition
- Information – The Heart of All Architecture Disciplines
- Relation Between Process and Data
- Enterprise Vs. Conceptual Vs. Logical
Subtypes, Supertypes, and Exercises
- Entity Subtypes
- Subtypes Examples
- Exercise: Super Types and Subtypes
- Data Model Representation
- Identifying Entities
  - Sample Entities
Entities, Attributes, and Exercises

Exercise: Entities
Dara Model Representation
Attributes
Levels of Data Models
Subject Area Model
Conceptual Data Model
Types of Models
Logical Data Model

Entities
- Entity Naming Best Practices
- Entity Definition Best Practices
- Common Errors with Entities
- Entities
- State Transition Diagrams
- The Basic Pattern
Attributes
- Attribute Properties and Domains
  - Domains
  - Creating a Domain
  - Domain Inheritance
Domains – Exercise: What Type of Domain?
Relationships
- Relationships Between Entities
- Relationship Types
  - Many to Many Relationships
    - Resolving a Many to Many Relationship
    - Example: Many to Many Relationships
  - Recursive Relationships
- Relationship Optionality
- Relationship Transferability
- Relationship Cardinality
  - Cardinality
- Relationship Best Practice (1)
- Relationship Best Practice (2)
- Summary: Logical Data Model Components
Course 2 Quiz
- Answer Key
- Final Quiz Scores
DMBoK And CDMP Preparation: Data Modeling Specialist Learning Plan – Progress Review

Course 3: Keys, Approaches, and Normalization

DMBoK And CDMP Preparation: Data Modeling Specialist Learning Plan - Courses
Course Goals
Data Modeling Exam Breakdown
Keys
- What are Keys?
- Key vocabularies
  - Candidate Keys
  - Primary Key
  - Entity Keys
  - Primary Key
  - Foreign Keys
    - Foreign Key
  - Alternate Keys
    - Alternate Key
  - Super Key/Candidate Key
- Inverted Key and Bitmap Index
- Composite Vs. Compound
- Keys: Natural Vs. Surrogate
Approaches
- Approaches – Bottom Up and Top Down
  - Approaches – Top Down
  - Approaches – Bottom Up
Exercise: Top Down Vs. Bottom Up

Exercise: Advantages and Disadvantages
Continued Approach
Why Produce a Data Model?

Normalization
- Why Normalize a Data Model?
- Normalization Rules
- Normalization Approaches
- 1^st Normal Form
- 2^nd Normal Form
- 3^rd Normal Form
- Normalization Mantra
- Normalizing an Example
- Adding a Primary Key
- Removing Attributes
- Business Rules
- Removing Multivalued Data Elements
- 1^st Normal Form Example
- 2^nd Normal Form
  - 2^nd Normal Form Example
- 3^rd Normal Form
  - 3^rd Normal Form Example
  - Third Normal Form (3NF) Qualifications
  - Example: Table NOT in 3NF
- Boyce Codd Normal Form (BCNF) – 3.5NF
- Fourth Normal Form (4NF)
  - Example: Table NOT in 4NF
- Fifth Normal Form (5NF)
  - Example: Table NOT in 5NF
- Summary
Quiz – Course 3
- Answer Key
- Final Quiz Scores
DMBoK And CDMP Preparation: Data Modeling Specialist Learning Plan – Progress Review

Course 4: Abstraction, Denormalization, Lineage, Data Models, Views & Partitioning

DMBoK And CDMP Preparation: Data modeling Specialist Learning Plan – Courses
Course Goals
Data Modeling Exam Breakdown
Abstraction
- Abstraction: The Basics
  - The Benefits
  - The Costs
- Abstraction
  - Abstraction: A Solution
  - Abstraction: Another Solution
Denormalization

Denormalize for Readability
Denormalize for Performance
When to Denormalize

Exercise – Denormalize Email Addresses
- Our 1NF Example
- A Possible Solution
- Denormalization Methods
- Standard Denormalization
- Repeating Group Denormalization
- Repeating Element Denormalization
- Summarization Denormalization
Data Lineage
- Data Lineage Considerations
- Data Lineage
- Data Lineage: E.G. Sox
Data Model Types
- Prism
- Physical Database Design Best-Practice
- Class, Prime, Modifier Qualifier Words
- Transforming From a Logical to Physical Data Model
- Hierarchical Data Models
- Network Data Models
- Logical Vs. Physical Data Types
- Non-Relational DB’s
- NOSQL Landscape
- Big Data Bingo
- Column Databases
- Columnar DBMS
- Key Value Data Stores
- NoSQL – Key Value Databases
Views and Partitioning
- Views
- Partitioning
- Natural Keys
- Virtual Keys
- Surrogate Keys
  - Can I Always Use Surrogate Key?
- Indexing
  - Why Have an Index?
  - Clustered Indexes
  - Unclustered Indexes
  - Atomicity
  - Consistency
  - Isolation
  - Durability
  - Base
Course 4 - Quiz

Answer Key
Final Quiz Scores

DMBoK And CDMP Preparation: Data Modeling Specialist Learning Plan – Progress Review

Course 5: Dimensional Models, Best Practices, and Notations

DMBoK and CDMP Preparation: Data Modeling Specialist Learning Plan – Courses
Course Goals
Data Modeling Exam Breakdown
Dimensional Data Models
- Relational and Dimensional Models
- Dimensional Model Features
  - A Dimensional Model
- Dimensions and Hierarchies
- Fact Tables
- How Dimensional Models Fit into the Data Warehouse
Exercise – Dimensions and Measures
- Answer Key
Dimensional Data Models Continued
- Slowly Changing Dimensions
  - The Problems Presented
    - Type 1 – Overwrite
    - Type 2 – Create New Record
    - Type 3 – Use an “Old” Field
  - How To Handle Slowly Changing Dimensions
- Data Quality in Data Warehouses (DW)
- Advanced Concepts
- The Need for Aggregate Tables
  - Aggregate Tables
  - Example – Aggregate Table
- Fact Tables Compared
  - Dimension Tables Compared
- Which Aggregates to Build
Exercise – Read a Data Model
- Example – Data Model
- Read a Data Model
- Read a Data Model – Discussion of Answers
Best Practices
- Model Representation and Layout
- Graphical Principles
- Place the Main Entity at the Centre of the Model
- Verb Phrases
- Pay Attention to Relationship Lines
- Data Modeling
- Data Governance and Models – Metadata Expansions
Data Modeling Notations
- Entity Relationship Symbols
- Information Engineering Model Diagram
- UML Class Diagram
  - Unified Modeling Language
  - The 13 UML 2.0 Diagrams
- Chen Model
- Barker Notation Diagram
- Idef1x Diagram
- XML
- XUML Diagram
- X-Entity Model Diagram
- Data Vault and Anchor Models
- Object Role Models (ORM)
Course 5 Quiz
- Answer Key
- Final Quiz Scores
Summary
DMBoK and CDMP Preparation: Data Modeling Specialist Learning Plan – Progress Review
Data Quotes