Chapters

 Analytics and ML Module Guide

Follow Data Dora from messy sensor evidence to an inspectable decision record.

  1. Data Dora studies an uneven sensor-event trail with gaps and irregular spacing on a review table.

    A sensor leaves messy evidence.

  2. Data Dora opens the processing stage into visible input, treatment, and output layers while the team inspects them.

    Dora makes the review step visible.

  3. A bounded analytic result branches toward separate alert and control-review choices as Data Dora explains the path.

    The result may change an alert or control choice.

  4. Data Dora presents sensor observations, transparent processing, a bounded result, and a separate validation record to the team.

    The team explains why anyone should trust it.

A visible analytics path connects uncertain observations to a reviewable decision record.

Start With the Module

This guide maps the current learning path for Analytics & ML. The module contains 59 chapters, linked below in the order learners can study them.

Chapters by Part

Edge Computing

Data Quality

Machine Learning

Anomaly Detection

Data & Sensor Fusion

Big Data & Cloud

Choose a Starting Point

If this subject is new, follow the parts and chapters in the order shown above. If you already have a specific design or troubleshooting question, use the relevant part heading and open the direct chapter link whose title matches that question.

Use the sidebar and site search for supporting material; use this chapter map as your main route through the module.

How to Use This Material

  • Start with the first chapter in the relevant part when the topic is unfamiliar.
  • Use direct chapter links for study plans, lab preparation, and review evidence.

← Back to All Modules