Introduction
Looker is a modern Business Intelligence (BI) and data analytics platform. It helps organizations explore and analyze data in real time. It also enables users to build dashboards and reports easily. Moreover, it uses a semantic modeling layer called LookML. As a result, teams can maintain consistent and governed data models. Therefore, Looker supports better and faster decision-making. In addition, it integrates well with cloud data platforms. Hence, it is widely used in modern analytics environments.

Learner Prerequisites

• Basic understanding of data concepts and databases
• Familiarity with SQL fundamentals
• Awareness of Business Intelligence concepts
• Exposure to any BI tool (optional but useful)
• Interest in data visualization and analytics

Table of Contents 

1. Introduction to Business Intelligence & Modern BI Concepts
1.1 Evolution of Business Intelligence and its growth over time
1.2 Differences between Traditional BI and Modern BI approaches
1.3 Key Components involved in BI Architecture
1.4 Role of Cloud technologies in BI transformation
1.5 Importance of Data-Driven Decision Making

2. Overview of Looker Platform
2.1 Introduction to Looker Interface and navigation
2.2 Key Features and Capabilities of Looker
2.3 Overview of Looker Architecture
2.4 Comparison of Looker with other BI tools
2.5 Common Use Cases of Looker in organizations

3. Getting Started with Looker Environment
3.1 Navigating the Looker User Interface effectively
3.2 Exploring datasets and managing connections
3.3 Understanding Explores and Views clearly
3.4 Running basic queries for analysis
3.5 Saving and sharing queries with teams

4. LookML Fundamentals (Data Modeling Basics)
4.1 Introduction to LookML and its purpose
4.2 Understanding Dimensions and Measures
4.3 Creating Views and Explores step by step
4.4 Managing data relationships and joins
4.5 Following best practices in LookML modeling

5. Data Exploration and Analysis
5.1 Building queries using Explore interface
5.2 Applying filters and sorting data efficiently
5.3 Using pivoting and drill-down analysis
5.4 Creating and using derived tables
5.5 Introduction to SQL Runner basics

6. Data Visualization Techniques
6.1 Introduction to Looker visualization options
6.2 Understanding different chart types
6.3 Customizing visualizations for clarity
6.4 Creating interactive dashboards effectively
6.5 Applying best practices for visualization

7. Dashboarding and Reporting
7.1 Building dashboards from Looks
7.2 Adding filters and interactive controls
7.3 Scheduling reports and setting alerts
7.4 Sharing and embedding dashboards
7.5 Managing dashboard performance

8. Data Governance and Security in Looker
8.1 Understanding role-based access control
8.2 Managing data permissions and security models
8.3 Using version control and collaboration features
8.4 Ensuring data consistency across reports
8.5 Monitoring usage and audit activities

9. Integration with Modern Data Stack
9.1 Connecting Looker to cloud data warehouses
9.2 Integrating with ETL and ELT tools
9.3 Embedding Looker into applications
9.4 Overview of API usage
9.5 Working within the Google Cloud ecosystem

10. Best Practices and Performance Optimization
10.1 Applying query optimization techniques
10.2 Using efficient data modeling strategies
10.3 Reducing query costs effectively
10.4 Improving dashboard performance
10.5 Avoiding common pitfalls and issues

Conclusion
In conclusion, this training builds a strong base in modern BI concepts. It also introduces core Looker features clearly. Moreover, learners gain skills in data modeling and visualization. As a result, they can create effective dashboards and reports. Therefore, participants will be ready to use Looker in real projects. In addition, they can support business teams with better insights. Hence, this training prepares learners for practical and professional use of Looker.