Data Modeling Corporate Training Course

1. Data Modeling Fundamentals

• Definition and Importance of Data Modeling
• Key Concepts and Terminologies
• Benefits of Effective Data Modeling

2. Types of Data Models (Conceptual, Logical, Physical)

• Conceptual Data Models
• Logical Data Models
• Physical Data Models
• Relationships and Cardinality

3. Importance of Data Modeling in Decision-Making

• Data-Driven Decision-Making Processes
• Role of Data Modeling in Business Strategy
• Linking Data Models to Organizational Goals

1. Entity-Relationship Diagrams (ERDs)

• Entity Types and Attributes
• Relationships and Relationship Cardinality
• Primary and Foreign Keys
• Normalization Concepts

2. Dimensional Modeling

• Fact and Dimension Tables
• Star Schema and Snowflake Schema
• Hierarchies and Aggregations
• Slowly Changing Dimensions

3. Hierarchical, Network, and Relational Models

• Hierarchical Model Overview
• Network Model Overview
• Relational Model Overview
• Pros and Cons of Each Model

1. Overview of Popular Data Modeling Tools

• ER/Studio
• PowerDesigner
• SQL Developer Data Modeler
• ERwin Data Modeler

2. Hands-on Experience with Data Modeling Software

• Tool Installation and Setup
• Creating Data Models
• Generating Scripts and Reports
• Collaboration and Version Control

3. Integrating Data Models with Existing Systems

• Reverse Engineering Existing Databases
• Forward Engineering to Implement Data Models
• Synchronizing Data Models with Database Changes
• Integration with Data Governance and Metadata Management

1. Assessing Data Quality

• Data Quality Dimensions
• Data Profiling Techniques
• Data Cleansing and Transformation
• Data Quality Assessment Tools

2. Techniques for Data Validation and Cleansing

• Data Validation Rules
• Constraint Enforcement
• Error Handling and Data Reconciliation
• Data Auditing and Monitoring

3. Data Profiling and Metadata Management

• Metadata Management Concepts
• Metadata Repositories
• Data Lineage and Impact Analysis
• Metadata Governance

1. Aggregation and Summarization Techniques

• Roll-Up and Drill-Down Operations
• Measures and Aggregates
• Summary Tables and Materialized Views
• Cube and OLAP Concepts

2. Handling Time-Series Data

• Temporal Data Modeling
• Effective Dating and Versioning
• Slowly Changing Dimensions (SCD) Techniques
• Time-Series Analysis Methods

3. Data Modeling for Big Data and NoSQL Databases

• Introduction to Big Data and NoSQL
• Data Modeling for Hadoop/Hive
• Document and Key-Value Data Models
• Graph and Columnar Data Models

1. Introduction to Predictive Analytics

• Predictive Modeling Concepts
• Classification vs. Regression
• Feature Selection and Engineering
• Model Evaluation Techniques

2. Statistical Modeling Techniques

• Linear Regression
• Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines

3. Building and Evaluating Predictive Models

• Data Preparation for Predictive Modeling
• Model Training and Testing
• Model Evaluation Metrics
• Overfitting and Regularization Techniques

1. Creating Data Model Documentation

• Documenting Data Entities and Attributes
• Data Dictionary and Glossary
• Diagram Notations and Conventions
• Data Model Versioning and Change Management

2. Data Visualization Techniques

• Charts and Graphs for Data Representation
• Dashboard Design Principles
• Interactive Data Visualization Tools
• Effective Data Communication Strategies

3. Presenting Data Insights to Stakeholders

• Storytelling with Data
• Communicating Key Findings and Trends
• Tailoring Presentations to Different Audiences
• Data Visualization Best Practices