Description

TABLE OF CONTENTS

UNIT 1 : Introduction to Data Modeling:

Definition of data modeling
Importance of data modeling in analytics
Overview of data modeling techniques and methodologies

UNIT 2 : Fundamentals of Analytics:

Introduction to analytics and its role in decision-making
Types of analytics: descriptive, diagnostic, predictive, and prescriptive analytics
Business Intelligence (BI) and analytics tools

UNIT 3 : Data Types and Structures:

Understanding different data types
Overview of data structures (tables, documents, graphs, etc.)
Relational vs. non-relational databases

UNIT 4 : Relational Database Concepts:

Basics of relational databases
Tables, rows, and columns
Primary keys, foreign keys, and relationships

UNIT 5 : Entity-Relationship Diagrams (ERD):

Creating and interpreting ERDs
Entities, attributes, and relationships
Cardinality and normalization

UNIT 6 : Dimensional Modeling:

Basics of dimensional modeling for data warehousing
Fact tables and dimension tables
Star schema and snowflake schema

UNIT 7 : Data Modeling Tools:

Overview of popular data modeling tools
Hands-on experience with a data modeling tool
Creating and modifying data models

UNIT 8 : Data Modeling Best Practices:

Design principles for effective data models
Ensuring data integrity and accuracy
Performance considerations in data modeling

UNIT 9 : Metadata Management:

Importance of metadata in data modeling
Strategies for metadata management
Impact of metadata on analytics and reporting

UNIT 10 : Data Modeling for Analytics Applications:

Designing data models for specific analytics applications
Incorporating predictive and prescriptive analytics in data models
Case studies of successful analytics data modeling projects

UNIT 11 : Data Quality and Governance:

Ensuring data quality in the modeling process
Data governance and compliance considerations
Data stewardship and accountability

UNIT 12 : Advanced Topics in Data Modeling:

Big data and data modeling
Streaming data and real-time analytics
NoSQL databases and their impact on data modeling

UNIT 13 : Practical Exercises and Projects:

Hands-on exercises to apply data modeling concepts
Real-world projects to reinforce learning
Peer collaboration and feedback