1: Introduction to Real-Time Analytics
1.1 Understanding Real-Time Analytics
1.1.1 The importance of real-time analytics in business
1.1.2 Key concepts and benefits of real-time data
1.1.3 Use cases and examples of real-time analytics in various industries
1.2 Overview of Tableau’s Live Data Capabilities
1.2.1 Introduction to live data connections in Tableau
1.2.2 Differences between live and extract connections
1.2.3 Benefits and limitations of live data connections
1.3 Planning for Real-Time Analytics
1.3.1 Defining objectives and requirements for real-time analytics
1.3.2 Identifying suitable data sources and integration needs
1.3.3 Designing a strategy for implementing real-time data connections

2: Connecting to Live Data Sources
2.1 Setting Up Live Data Connections
2.1.1 Connecting Tableau to various live data sources (databases, APIs, cloud services)
2.1.2 Configuring data source connections for real-time access
2.1.3 Managing authentication and connection settings
2.2 Optimizing Live Data Connections
2.2.1 Ensuring data source performance and reliability
2.2.2 Configuring query settings and caching options for efficiency
2.2.3 Handling large datasets and complex queries
2.3 Managing Data Source Refreshes
2.3.1 Setting up automatic data refresh intervals
2.3.2 Handling data source interruptions and connectivity issues
2.3.3 Monitoring and troubleshooting connection problems

3: Designing Real-Time Dashboards
3.1 Creating Interactive Dashboards
3.1.1 Designing dashboards that reflect real-time data
3.1.2 Adding interactive elements (filters, parameters, actions)
3.1.3 Balancing real-time interactivity with performance
3.2 Visualizing Real-Time Data
3.2.1 Best practices for visualizing live data
3.2.2 Using dynamic charts and visualizations for real-time insights
3.2.3 Displaying trends, anomalies, and patterns in real-time
3.3 Enhancing User Experience
3.3.1 Designing for usability and clarity in real-time dashboards
3.3.2 Implementing user controls and interactive features
3.3.3 Ensuring responsiveness and accessibility

4: Advanced Real-Time Analytics Techniques
4.1 Leveraging Real-Time Data Streams
4.1.1 Integrating Tableau with real-time data streams (e.g., IoT, event data)
4.1.2 Handling continuous data updates and streaming data sources
4.1.3 Visualizing real-time data streams effectively
4.2 Creating Real-Time Alerts and Notifications
4.2.1 Setting up alerts and notifications based on real-time data
4.2.2 Configuring automated email or dashboard alerts for specific conditions
4.2.3 Using Tableau’s alerting features for timely decision-making
4.3 Advanced Real-Time Analytics Scenarios
4.3.1 Implementing advanced analytical techniques with real-time data
4.3.2 Handling high-velocity data and ensuring real-time performance
4.3.3 Case studies and examples of advanced real-time analytics in practice

5: Performance and Scalability
5.1 Optimizing Real-Time Dashboard Performance
5.1.1 Techniques for improving the performance of real-time dashboards
5.1.2 Managing high-frequency data updates and minimizing latency
5.1.3 Best practices for efficient real-time data visualization
5.2 Scaling Real-Time Analytics Solutions
5.2.1 Planning for scalability and handling large volumes of real-time data
5.2.2 Configuring Tableau Server or Tableau Online for high performance
5.2.3 Managing resource allocation and optimizing server settings
5.3 Troubleshooting Real-Time Data Issues
5.3.1 Identifying and resolving common issues with real-time data connections
5.3.2 Using diagnostic tools and performance monitoring features
5.3.3 Implementing solutions for data connectivity and performance problems

6: Real-World Applications and Best Practices
6.1 Case Studies in Real-Time Analytics
6.1.1 Analyzing real-world examples of real-time analytics implementations
6.1.2 Lessons learned and best practices from various industries
6.1.3 Discussion of different approaches to real-time data analysis
6.2 Implementing Real-Time Analytics in Business
6.2.1 Practical applications and use cases for real-time analytics in business
6.2.2 Designing real-time analytics solutions to meet specific business needs
6.2.3 Aligning real-time analytics with business goals and strategies

7: Capstone Project and Review
7.1 Capstone Project Overview
7.1.1 Guidelines for the capstone project: Building a real-time analytics dashboard
7.1.2 Selecting a data source and defining real-time requirements
7.1.3 Designing and implementing a complete real-time analytics solution
7.2 Project Work and Presentations
7.2.1 Working on individual capstone projects
7.2.2 Peer review and group feedback sessions
7.2.3 Final project presentations
7.3 Final Review and Next Steps
7.3.1 Summary of key concepts and techniques
7.3.2 Exploring advanced topics and further learning resources
7.3.3 Course conclusion and participant feedback