1: Introduction to Kibana for Data Analysts
1.1 Overview of Kibana’s role in the data analysis process
1.2 Key features of Kibana for data exploration and visualization
1.3 Use cases for data analysts: Turning data into insights
1.4 Kibana’s integration with the Elastic Stack

2: Exploring Data with Kibana’s Discover Tab
2.1 Connecting Kibana to Elasticsearch indices
2.2 Navigating the Discover tab for data exploration
2.3 Querying and filtering data in Kibana
2.4 Saving and sharing data queries for analysis

3: Building Basic Visualizations for Data Analysis
3.1 Introduction to Kibana visualizations
3.2 Creating bar charts, line graphs, and pie charts for data analysis
3.3 Understanding aggregation types and their impact on visualizations
3.4 Filtering and refining visualizations with queries and filters

4: Advanced Data Visualization Techniques
4.1 Working with time-series data visualizations
4.2 Building heatmaps, area charts, and data tables
4.3 Visualizing geo-spatial data: Creating maps and region charts
4.4 Combining multiple visualizations for in-depth analysis

5: Building Analytical Dashboards
5.1 Introduction to dashboards in Kibana
5.2 Designing and building dashboards for data-driven decision-making
5.3 Adding multiple visualizations to a dashboard
5.4 Customizing dashboards for different analytical needs

6: Optimizing Data Queries for Analysis
6.1 Using Kibana Query Language (KQL) for powerful queries
6.2 Applying filters and search techniques to optimize results
6.3 Best practices for querying large datasets efficiently
6.4 Managing and saving query results for repeated analysis

7: Using Kibana for Trend Analysis
7.1 Identifying trends and patterns in data using visualizations
7.2 Creating time-series visualizations for temporal data
7.3 Analyzing seasonality and anomalies in the data
7.4 Building interactive dashboards for monitoring trends

8: Sharing and Collaborating on Data Insights
8.1 Exporting and sharing visualizations and dashboards
8.2 Collaborating on data insights using Kibana
8.3 Embedding Kibana visualizations in reports or external tools
8.4 Leveraging Kibana’s reporting features for data presentations

9: Enhancing Insights with Machine Learning
9.1 Introduction to machine learning capabilities in Kibana
9.2 Using anomaly detection for identifying outliers in data
9.3 Visualizing machine learning results for predictive analysis
9.4 Applying machine learning insights to business data

10: Hands-On Project: Building an Analytical Dashboard
10.1 Project overview: Analyzing real-world business data with Kibana
10.2 Designing visualizations to identify key trends and metrics
10.3 Building an interactive dashboard for data analysis
10.4 Sharing and presenting the final analysis to stakeholders

11: Conclusion and Best Practices
11.1 Recap of key concepts and tools for data analysts
11.2 Best practices for building effective dashboards and visualizations
11.3 Common challenges and troubleshooting tips
11.4 Resources for further learning and community engagement