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Chapters

This textbook is organized into 15 chapters covering 200 concepts.

Chapter Overview

  1. Introduction to Organizational Analytics - Introduces HR data systems, relational database limitations, and the case for graph-based organizational analytics.
  2. Graph Database Fundamentals - Covers graph data models, nodes, edges, properties, schema design, query languages, traversals, and performance.
  3. Employee Event Streams - Explores the sources of organizational data and how to normalize and timestamp events.
  4. Data Pipelines and Graph Loading - Covers staging, ETL, batch and stream processing, real-time ingestion, and data quality.
  5. Modeling the Organization - Builds the graph data model for employees, departments, communication, positions, and projects.
  6. Ethics, Privacy, and Security - Addresses consent, anonymization, privacy by design, access control, and record retention.
  7. Graph Algorithms: Centrality and Pathfinding - Introduces centrality measures, PageRank, shortest path, Dijkstra, BFS, and DFS.
  8. Graph Algorithms: Community and Similarity - Covers community detection, similarity algorithms, graph metrics, and subgraph analysis.
  9. Natural Language Processing - Introduces NLP fundamentals, sentiment analysis, topic modeling, LLMs, and summarization.
  10. Machine Learning and Graph ML - Covers ML fundamentals, graph neural networks, node embeddings, and bias in analytics.
  11. Organizational Insights - Applies graph and NLP techniques to detect influence, silos, vulnerability, and retention patterns.
  12. Recognition, Alignment, and Innovation - Uses analytics for recognition, strategy alignment, ideation, and inclusion analytics.
  13. Talent Management and Placement - Covers mentoring, skill gaps, placement, career guidance, onboarding, and merger integration.
  14. Reporting and Dashboards - Covers reporting, dashboard design, visualization, real-time discovery, and alerting.
  15. Capstone Projects and Integration - Integrates all skills into graph libraries, pipelines, health scoring, and continuous improvement.

How to Use This Textbook

Chapters are sequenced so that each chapter builds on concepts from earlier chapters. Foundational topics like graph databases and event streams appear first, followed by algorithms and NLP, then applied insights and capstone projects. You can follow the chapters in order for a complete learning path, or jump to specific chapters if you already have prerequisite knowledge.

Note: Each chapter includes a list of concepts covered. Make sure to complete prerequisites before moving to advanced chapters.