1. Introduction to IBM i2 Analyst’s Notebook

• Overview of IBM i2 Analyst’s Notebook:
  • Purpose and applications in data analysis, law enforcement, intelligence, security, and investigations.
  • Key features and tools within the software.
  • Understanding charts, nodes, links, and workspaces.
• User Interface:
  • Navigation through the interface.
  • Customizing toolbars and views.
  • Overview of the ribbon, workspace, and quick access features.

2. Getting Started with Data

• Importing and Managing Data:
  • How to import data from various sources such as CSV, Excel, text files, and databases.
  • Understanding the import dialog and options for mapping data fields to Analyst’s Notebook objects.
  • Working with i2 iBase data integration for advanced database connectivity.
• Data Cleaning and Preprocessing:
  • Techniques to clean and structure raw data for meaningful analysis.
  • Handling missing or incomplete data.

3. Working with Charts

• Creating a Chart:
  • Steps for creating different types of charts: link, temporal, and geospatial.
  • Adding nodes and links manually or through data imports.
• Node Types and Linking Data:
  • Different types of nodes (people, organizations, locations, etc.).
  • How to create, modify, and link nodes for various entities.
  • Understanding the significance of link types (association, ownership, communication, etc.).
• Chart Layout and Customization:
  • Modifying chart elements (node size, link appearance, colors, and labels).
  • Organizing charts for clarity using automatic layout or manual adjustment.
  • Grouping and filtering nodes to highlight key data.

4. Data Analysis and Visualization

• Basic Analysis Techniques:
  • Exploring connections and relationships between entities.
  • Visualizing clusters and key patterns in the data.
  • Highlighting significant trends using layout and filters.
• Temporal Analysis:
  • Creating and analyzing time-based events and activities.
  • Using time-based charts to visualize sequences and overlaps.
  • Investigating patterns over time (e.g., crime trends, financial transactions).
• Geospatial Analysis:
  • Visualizing location-based data on maps (geospatial charts).
  • Plotting addresses, geographic coordinates, and other spatial data.
  • Analyzing proximity, distances, and patterns across locations.

5. Advanced Analytical Techniques

• Link Analysis:
  • Identifying key relationships and hidden connections in complex data sets.
  • Using clustering techniques to group similar data points.
• Pattern Detection:
  • Detecting common patterns, such as fraud detection or criminal activity.
  • Analyzing recurring behaviors and events within data.
• Entity Resolution:
  • Handling duplicate records and resolving multiple representations of the same entity.
  • Techniques for consolidating data from different sources.
• Automated Analysis:
  • Using pre-configured analysis tools for automatic detection of patterns and anomalies.

6. Working with Queries and Filters

• Search Capabilities:
  • Using advanced search options to query and filter data.
  • Applying multiple filters to narrow down results.
• Defining and Applying Filters:
  • Customizing filters based on node attributes (e.g., location, date, type of relationship).
  • Creating custom queries for data analysis.

7. Reporting and Sharing Results

• Creating Reports:
  • Exporting charts into various formats like PDF, Excel, or image files.
  • Creating visual reports that summarize key findings.
  • Adding annotations, legends, and descriptions to charts for clarity.
• Collaboration and Sharing:
  • Sharing charts and reports with stakeholders or team members.
  • Using the software’s collaboration features for joint analysis.
  • Saving and managing reports in shared directories or through cloud-based systems.

8. Security and Data Privacy

• Managing Data Access:
  • Setting permissions and access controls for sensitive data.
  • Understanding security features related to data protection.
• Encrypting Charts and Reports:
  • Techniques to encrypt and secure charts for confidential information.
  • Managing data integrity and audit trails.

9. Best Practices and Efficiency

• Chart Organization and Management:
  • Managing complex charts effectively by using templates and grouping.
  • Using naming conventions and documentation to maintain clarity in large projects.
• Optimizing Performance:
  • Handling large datasets efficiently.
  • Best practices for performance optimization (e.g., working with large datasets, avoiding crashes).

10. Advanced Features (Optional, depending on the course level)

• Scripting and Automation:
  • Automating common tasks using Python scripts or built-in tools.
  • Writing custom scripts to extend i2 Analyst’s Notebook functionality.
• Customizing i2 Analyst’s Notebook:
  • Using external modules or plugins to enhance analysis.
  • Customizing the software’s interface for specific use cases.