Overview
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Appendix A - Diagram Templates by Step
Appendix B - Mapping Concepts to Real-World Data Stacks
Appendix C - Readiness Checklists (Step N -> Step N+1)
Appendix D - Glossary (Canonical Definitions)

Course Setup and the Incremental Ladder
Why "Facts to Forecasts"
How to Use This Course
The Incremental Ladder (Step 0 -> Step 7)
The Course Lenses
Diagram Legend and Notation Types

What Is a Data Analytics & Decision System?
Reports vs Analytics vs Decision Systems
Descriptive to Prescriptive Thinking
The Decision Loop

Data as a Model of the World
Entities, Events, Relationships
Operational vs Analytical Views
Domain Alignment

Facts, Metrics, and Dimensions
Facts and Dimensions
Events as the Building Block
From Facts to Metrics

People and Roles in Data Systems
Producer Roles
Modeling and Analysis Roles
Decision-Makers and Responsibilities

Diagramming Data Analytics Systems
End-to-End Flow Diagrams
Lineage and Dependency Graphs
Decision Flow Maps

Step 0 Data Modeling for Analytics
Entities vs Events
Identifiers, Keys, Relationships
Slowly Changing Dimensions

Step 0 Descriptive Statistics
Core Aggregations
Distributions and Percentiles
Variability and Trends

Step 0 Analytical Queries
Group-By Thinking
Joins for Meaning
Rolling Windows and Comparisons

Step 0 Common Analytical Patterns
Cohorts and Funnels
Retention and Churn
Conversion and Attribution

Step 0 First Analytics Environment
A Minimal Analytical Database
Core Tables and Views
From Manual Queries to First Dashboards

ETL vs ELT (Conceptual)
ETL and ELT Primitives
Modern ELT Context
Trade-Offs

Sources of Data
Operational Databases and APIs
Event Streams and Logs
External and Third-Party Data

Batch vs Streaming Pipelines (Conceptual)
Batch Ingestion
Streaming and Near-Real-Time
Selecting the Mode

Pipeline Orchestration
DAGs of Tasks and Jobs
Scheduling, Dependencies, Retries
Failures and Idempotency

Data Cleaning and Transformation
Type Normalization and Units
Deduplication and Reconciliation
Missing and Noisy Data

Data Quality and Validation
Expectations and Rules
Checks at Ingestion vs in the Warehouse
Alerting and Health Reporting

Analytical Storage Architectures
Warehouses
Lakes and Lakehouse Concepts
Selecting an Analytical Storage Architecture

Schemas for Analytics
Dimensional Models
Fact and Dimension Tables in Practice
Normalized vs Denormalized Trade-Offs

Tables, Views, and Semantic Layers
Raw vs Curated Layers
Views and Semantic Layers
Logical vs Physical Models

Partitioning, Clustering, and Performance
Partitioning Strategies
Indexing and Clustering Concepts
Cost and Performance

Governance, Access, and Security
Access Control Boundaries
Masking and Anonymization
Privacy and Compliance Posture

Multi-Environment and Multi-Region Warehousing
Dev, Stage, and Prod for Data
Residency and Multi-Region Constraints
Replication and DR Concepts

BI Tools and Visualization
What BI Tools Provide
Choosing Chart Types
Visual Storytelling Patterns

Dashboards, Reports, and Ad Hoc Analysis
Dashboard Types
Static Reports vs Interactive Exploration
Drill-Down and Cross-Filtering

Semantic Metrics Layer
Define Metrics Once
Preventing Metric Fragmentation
Serving Metrics to Tools and Systems

Designing Dashboards for Decisions
Leading vs Lagging Indicators
Guardrails and Trade-Offs
Owners and Decisions

Self-Service Data Culture
Analyst vs Self-Serve Flows
Templates, Curated Datasets, Training
Empowerment with Governance

BI Platform Operations
Content Sprawl and Staleness
Performance Tuning for BI Workloads
Monitoring Usage and Usefulness

Why Experimentation?
Correlation vs Causation
Experiments as Intervention Tests
When Not to Experiment

Experiment Design Basics
Treatment and Control
Randomization and Assignment
Sample Size and Duration Intuition

Experimentation System Architecture
Exposure Logging and Event Capture
Experiment Configuration Surfaces
Bucketing, Flags, and Rollout Controls

Metrics for Experiments
Primary vs Secondary Metrics
Guardrail Metrics for Safety
Metric Alignment with Decisions

Analyzing Experiments (Conceptual)
Differences Between Groups
Confidence and Uncertainty
Overlaps and Multiple Experiments

Operationalizing Experimentation
Experiment Review Processes
Libraries of Learnings
Integrating into Rhythms

From Descriptions to Predictions
Prediction-Shaped Questions
Supervised Learning at a High Level
Prediction as a Decision Input

Data for Predictive Models
Feature Engineering Concepts
Splits and Leakage
Drift Concepts

Evaluating Predictive Models
Accuracy, Error, Calibration
Precision vs Recall Trade-Offs
Offline vs Online Evaluation

Forecasting Time-Series
Time-Series Structure
Horizons and Granularity
Uncertainty and Intervals

Model Serving and Integration
Batch vs Real-Time Scoring
Feature Stores Conceptually
Wiring Predictions into Flows

Monitoring Models in Production
Performance Over Time
Drift, Label Delay, Feedback Gaps
Retraining and Lifecycle Management

Decisions and Decision Frameworks
Repeated vs One-Off Decisions
Thresholds, Playbooks, Policies
Human vs Automated Boundaries

Decision Rules and Policy Engines
Rules Engines Conceptually
Combining Metrics and Model Outputs
Versioning and Auditing Rules

Resource Allocation and Optimization (High-Level)
Allocation Problems
Optimization as Constraint Satisfaction
Heuristics vs Exact Methods

Human-in-the-Loop Decision Systems
Recommendations vs Auto-Approval
Escalations, Overrides, and Annotations
UX for Decision Makers

Observability and Feedback Loops for Decisions
Logging Decisions and Outcomes
Post-Decision Analysis and Recalibration
Continuous Improvement Loops

End-to-End Analytics Platform Architecture
End-to-End Flow in a Platform Architecture
Logical vs Physical Components
Control Planes and Platform Control

Multi-Tenant and Multi-Domain Data Platforms
Multi-Team Platforms and Shared Failure Domains
Data Contracts and Domain Ownership
Autonomy vs Standardization in Multi-Domain Platforms

Governance, Ethics, and Compliance
Access, Privacy, Usage Boundaries as Platform Properties
Bias and Fairness Concerns in Data and Decision Systems
Documentation, Audits, and Accountability for Reviewable Behavior

Operating Analytics and Decision Systems
SLOs for Pipelines, Warehouses, and BI
Incident Handling and Data Outage Playbooks
Capacity Planning and Cost Management

Organizational Adoption and Data Culture
Maturity Transitions - From Reports to Decision Systems
Training, Documentation, and Evangelism as System Interfaces
Incentives and Alignment - Rewarding Correct Decisions

Reference Architectures and Maturity Models
Early Stage Reference Architecture - One Database, One Truth Boundary
Growth Stage Reference Architecture - From Single Slice to Shared Pipelines
Mature Stage Reference Architecture - Unified Semantics, Decisions, and Governance

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What Is a Data Analytics & Decision System?

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